What Tech Employers Want (It's Not What's on the Job Description)
By
Ethan Fahey
•
Feb 27, 2026
Picture this: you’re a senior ML engineer or LLM specialist scanning a 20-bullet job description in 2025. It asks for production experience in PyTorch, TensorFlow, and JAX, plus Kubernetes, Terraform, and “hands-on research” while somehow expecting both deep IC focus and exceptional cross-functional communication. It’s no surprise that many candidates close the tab wondering if anyone truly checks every box.
Behind the scenes, most of these postings are stitched together from templates and multiple stakeholder wish lists. In reality, hiring teams usually prioritize a much narrower set of outcomes: how you ship, how you collaborate, and how you drive business impact with AI systems. At Fonzi AI, we see this pattern across hundreds of startup and scale-up hiring processes. As a curated marketplace for experienced AI, ML, infrastructure, full-stack, backend, and data engineers, Fonzi focuses on surfacing the signals that actually matter and structures its 48-hour Match Day to connect serious candidates with salary-transparent companies. In this article, we’ll break down the real skills employers look for, how AI is being used (and sometimes misused) in hiring, and how to position yourself effectively in the 2025–2026 market.
Key Takeaways
AI employers evaluate systems thinking, product sense, and communication, not just LeetCode performance. Analytical thinking has held the top position among employer needs for five consecutive years because machines cannot replicate navigating ambiguity and solving novel problems effectively.
Many companies now use AI in hiring, but tools vary wildly in quality. Some add noise or bias, while responsibly built platforms like Fonzi AI use bias-audited models and human review to increase fairness and signal quality.
Fonzi AI’s Match Day compresses weeks of hiring into approximately 48 hours, giving senior engineers fast, high-signal access to curated AI startups and high-growth tech companies with salary transparency from the start.
Soft skills matter more than ever in technical environments. With 44% of essential work skills set to transform within the next five years, employers seek individuals who can adapt, communicate effectively, and lead without formal authority.
The Skills Tech Employers Actually Optimize For (Beyond the JD)
Across FAANG-level firms and AI startups, patterns converge on a handful of core capabilities that predict impact more than any single backend or frontend framework or tool. This isn’t a generic list of transferable skills; it’s a structured overview tied to how modern tech interviews actually evaluate candidates, especially for AI, ML, and infrastructure roles.
The skill set below mixes professional skills (both soft skills and hard skills) because that’s how employers actually think about candidates. They’re not separating “technical skills” from “people skills” in a spreadsheet; they’re asking whether you can solve their problems, work with their team, and grow with their company.
Some of these key skills are hard to capture with keyword screens; one reason platforms like Fonzi AI lean on structured signals and human review instead of pure ATS filters. Let’s break down each area.
Communication Skills for Engineers (Written, Spoken, and Asynchronous)
Communication skills have become a top-3 requirement for senior engineers, especially in remote software engineering and/or globally distributed AI teams. The ability to communicate effectively across time zones, functions, and technical depths isn’t optional anymore; it’s foundational.
Written communication skills show up everywhere in technical work: design docs, RFCs, PR descriptions, experiment reports, and incident postmortems. These artifacts are often what hiring managers review when they ask for a “portfolio” or “GitHub.” Strong written communication means clarity, structure, and the ability to explain complex tradeoffs to readers with varying technical backgrounds.
Verbal communication matters in interviews and daily work. Can you explain a complex model, system design, or architectural tradeoff to non-specialist stakeholders, product managers, ops teams, and founders in a 30-minute Zoom call? Many employers weigh this heavily because senior engineers spend significant time translating technical decisions for business audiences.
Asynchronous collaboration is the modern workplace default. Using digital tools like GitHub, Notion, Slack, or Linear to communicate decisions, log experiments, and unblock others without meetings demonstrates mature interpersonal skills. Employers want evidence that you can drive progress without requiring synchronous face time.
Resume and interview tips:
Quantify impact in bullet points: “Reduced model inference latency by 40% through architecture redesign” beats “Worked on model optimization”
Practice 2-minute “explain your last project” stories tailored to different audiences (PMs vs. staff engineers)
Include links to writing samples, design docs, or technical blog posts in your profile
Problem-Solving, Systems Thinking, and Debugging Under Uncertainty
Top employers care less about trick puzzles and more about how candidates reason through ambiguous, production-grade problems. Problem-solving skills in tech aren’t about knowing the answer; they’re about demonstrating a rigorous process for finding it.
Systems thinking means being able to trace an incident across services, data pipelines, and model behavior, then propose realistic mitigations. When an LLM-powered feature starts returning poor results, can you identify whether the issue is data quality, prompt drift, infrastructure latency, or model degradation? This critical thinking is what separates senior engineers from those who can only work on isolated components.
Structured problem solving involves breaking an ill-defined issue into hypotheses, experiments, and measurable outcomes. For example, improving LLM latency by 35% or reducing infrastructure cost by $50K monthly requires framing the problem, identifying constraints, and executing systematically.
How this shows up in interviews: Expect debugging questions, incident-response scenarios, or “design a v1 system” prompts where there’s no single correct answer. Interviewers are evaluating your reasoning process, not just your conclusion.
Preparation advice: Have 2-3 stories ready demonstrating how you diagnosed and fixed a real production problem in 2023-2025, including metrics and tradeoffs. Critical thinking and other soft skills shine when you can articulate what you tried, what failed, and what you learned.
Adaptability, Learning Speed, and Continuous Upskilling
AI tooling and infrastructure stacks are changing quarterly. Employers in 2025 weigh learning speed and curiosity heavily: 87% of surveyed employers expect increasing use of AI and big data technologies, and they need engineers who can keep pace.
Adaptability skills mean moving from TensorFlow to PyTorch, or from on-prem GPUs to a managed inference platform, without months of ramp time. The specific framework matters less than your demonstrated ability to learn new skills quickly and apply them effectively.
How to show this:
Highlight new languages or frameworks learned since 2023
Point to open-source contributions or upskilling in LLMOps, vector databases, or emerging tools
Include specific examples: “Re-architected legacy service from Java monolith to Go microservice with zero downtime in Q4 2024”
Fonzi AI’s vetting process actively surfaces engineers who show this pattern of self-driven upskilling, not just static titles from past employers. We’re looking for evidence that your career path includes continuous professional growth and adaptation to industry trends.
Teamwork, Collaboration, and Remote-Ready Interpersonal Skills
Modern AI teams are cross-functional: research, data, infrastructure, product, and security often work as one pod. Teamwork skills aren’t about being friendly—they’re about being reliable, communicative, and able to navigate disagreement productively.
Core teamwork traits tech employers seek:
Reliability in sprints and hitting commitments
Willingness to own unglamorous work (documentation, on-call, technical debt)
Openness to code review feedback and iterating based on input
Remote-ready interpersonal skills are especially relevant in hybrid setups: proactive status updates, inclusive decision-making, and handling disagreement in writing without creating conflict. Maintaining a positive work environment across time zones requires emotional intelligence and self-awareness about how your communication lands.
Examples to include:
Mentoring junior developers or running onboarding for new hires
Leading a reading group on Transformers or LLM techniques in 2024
Co-leading an incident postmortem that improved team processes
Fonzi AI collects signal on collaboration styles through structured questions and reference checks, so employers don’t rely solely on a 45-minute interview vibe.
Self-Management, Time Management, and Ownership
Senior engineers in high-growth startups are expected to “run their own roadmap” without heavy project management overhead. Time management skills and ownership aren’t nice-to-haves—they’re table stakes for anyone billing themselves as senior.
Self-management behaviors include:
Breaking down ambiguous goals into actionable tasks
Estimating work realistically and communicating blockers early
Hitting deadlines without constant follow-up from managers
This connects to productivity in remote engineering jobs: blocking focus time, managing on-call rotations, and balancing experiments versus shipping. The ability to prioritize tasks effectively and maintain a strong work ethic without supervision is incredibly valuable.
How to showcase ownership: Lead with examples of owning a feature or service from spec to production in a specific timeframe. “Shipped customer-facing LLM search feature in Q2 2024, driving 23% increase in user engagement” tells a clearer story than “Worked on search infrastructure.”
Fonzi AI’s candidate profiles encourage this narrative by highlighting “initiatives owned” and business impact, not just job titles and dates.
Technical Depth: From Data Literacy to AI/ML and Infrastructure Mastery
While soft skills are non-negotiable, tech employers still require sharp technical fundamentals tailored to the role. Technical knowledge expectations vary by position, but baseline competencies are consistent across AI, ML, infrastructure, and backend roles.
Data literacy is foundational: being comfortable reading dashboards, querying data with SQL and Python, and turning metrics into decisions about models or systems. Data analysis isn’t just for data scientists; every technical role benefits from being able to interpret what the numbers mean.
Senior candidates are expected to:
Design and critique architectures (from LLM retrieval pipelines to globally distributed microservices)
Understand tradeoffs between consistency, availability, and performance
Navigate the Microsoft Office Suite and digital tools for documentation and collaboration (yes, even senior engineers need to write reports and create presentations)
Practical advice: Keep a short, up-to-date “tech stack snapshot” ready for resumes and Fonzi AI profiles. List specific tools, frameworks, and platforms used since 2023. This helps hiring managers quickly understand your technical currency.
Later, we’ll dive specifically into what’s unique about AI, ML, and LLM skill expectations compared with traditional software engineering roles.
Leadership, Mentoring, and Influence Without Title
“Senior” in 2025 usually implies scope and influence, not just years of experience or whether you’re an IC versus a manager. Leadership skills don’t require a formal title, they require demonstrated impact on people and decisions.
Informal leadership looks like:
Driving technical decisions across a pod or team
Aligning stakeholders around an experiment plan or architectural direction
Standardizing evaluation metrics for LLM features that other teams adopt
Mentoring is a key signal: Onboarding new hires, reviewing PRs thoughtfully with teaching intent, or running internal workshops on topics like RAG or observability demonstrate strong leadership qualities and investment in others’ professional growth.
Interview preparation: Have 1-2 stories showing how you moved a team or stakeholder from disagreement to alignment on a technical direction. Conflict management and the ability to navigate productive conversations under pressure show effective team management capability.
Fonzi AI surfaces leadership behaviors in candidate summaries, helping hiring managers quickly see who can operate at Staff+ expectations.
How AI Is Changing Hiring and Where It Helps vs. Hurts
By mid-2025, many companies use AI in their recruiting stack, including screening, scheduling, assessments, and more. But quality and ethics vary wildly. Some tools add clarity and reduce bias. Others create noise, reinforce inequities, and frustrate job seekers with opaque processes.
Understanding how AI is used helps you navigate strategically and protect your experience. The difference between responsible and irresponsible AI hiring tools can mean the difference between landing interviews and being auto-rejected by an algorithm that never understood your work.
Fonzi AI’s stance is clear: AI should reduce friction and bias while increasing signal. It shouldn’t replace human judgment or turn candidates into data points. The following sections explain where traditional hiring breaks down, how AI tools can help or hurt, and how Fonzi AI’s approach differs.
Where Traditional Hiring Breaks Down for Senior Engineers
Traditional hiring is slow, opaque, and often random for experienced engineers. The pain points are familiar:
Weeks of slow back-and-forth: Submitting applications, waiting days for recruiter screens, scheduling conflicts, and multi-week gaps between interview rounds
Vague feedback: Generic rejections with no actionable information about what went wrong
Misaligned salary expectations: Discovering after four rounds that the budget is 30% below your target
Ghost jobs: Interviewing for roles that aren’t actually ready to hire or have already been filled
Many engineers report submitting 100+ applications in 2024 and hearing almost nothing back, or being ghosted after final rounds with no closure.
This environment particularly frustrates experienced AI, ML, and infrastructure engineers whose work is hard to summarize in keyword form. How do you reduce “built a distributed training infrastructure that cut model training time by 60%” into a checkbox for an ATS system?
The job outlook for tech talent remains strong, but the process for connecting that talent with opportunities remains broken. There’s a clear need for a different model that adds structure, fairness, and time-bound decision-making.
AI in Hiring: Risky Shortcuts vs. Responsible Use
Some AI tools in recruiting have been rightly criticized for opacity, bias, and reinforcing inequities. Not all AI hiring is created equal.
Risky patterns include:
Black-box scoring of resumes with no transparency about the criteria
Over-reliance on keyword matches that filter out qualified candidates
Automated rejections with no human oversight or appeal process
Training data that encodes historical biases (favoring certain schools, employers, or demographics)
Responsible patterns look different:
Transparent criteria that candidates can understand and optimize for
Bias-audited models with regular testing and refinement
Humans are always in the loop for final decisions
AI handles logistics and pattern-matching while humans evaluate substance
Responsible AI in hiring should help recruiters spend more time talking to people and less time on repetitive logistics or manual screening. The goal is augmentation, not replacement.
Fonzi AI explicitly invests in fraud detection, structured hiring and evaluation, and fairness audits. We do not auto-reject candidates based solely on an algorithmic score. AI helps surface patterns and reduce administrative burden; humans make the decisions that matter.
Comparison Table: Traditional Hiring vs. Generic AI Tools vs. Fonzi AI Match Day
The table below compares three hiring approaches across dimensions that matter most to senior engineers:
Dimension | Traditional Hiring | Generic AI Recruiting Tools | Fonzi AI Match Day |
Typical Timeline | 4-8 weeks from application to offer | 2-4 weeks (faster screening, same slow processes) | ~48 hours per Match Day event; offers often within 1-2 weeks |
Signal Quality | Variable; depends on recruiter bandwidth and attention | Often low; keyword matching misses context | High; structured profiles, bias-audited evaluation, human review |
Salary Transparency | Usually revealed late in process | Rarely included | Employers commit to salary ranges upfront |
Bias Controls | Minimal; depends on individual interviewers | Often problematic; black-box algorithms | Bias-audited models with human oversight |
Candidate Experience | Frustrating; ghosting common | Impersonal; automated rejections | Concierge support; clear expectations and feedback |
Best For | Companies with unlimited recruiting resources | High-volume, entry-level hiring | Senior AI/ML/infra engineers seeking curated opportunities |
Fonzi AI is designed around high-signal matches, not volume applications. The goal is quality connections between elite engineers and committed employers.
Inside Fonzi AI’s Match Day: A High-Signal Way to Show Your Skills
Match Day is a structured hiring event where vetted engineers and committed employers meet in a tight, 48-hour window. Instead of applying to hundreds of jobs and hearing nothing back, you participate in a curated process where companies have already committed to hiring.
Fonzi AI focuses on AI, ML, LLM, infrastructure, and high-caliber full-stack/backend roles at startups and high-growth tech companies. Employers commit to salary ranges upfront, reducing guesswork and negotiation drama later.
The economics are simple: Candidates join for free. Fonzi AI charges employers an 18% success fee when a hire is made. This aligns incentives: we succeed when you get hired.
Step 1: Apply Once, Build a High-Signal Profile
The initial application focuses on what matters: experience (3+ years), tech stack, domains (AI/ML, LLMs, infrastructure, backend, data), and recent impact. This isn’t another generic job application; it’s building a portfolio that demonstrates your relevant skills and capabilities.
Fonzi AI uses both human reviewers and AI assistance to parse projects, artifacts (GitHub, papers, demos), and outcomes, not just job titles. We’re looking at substance: what did you actually build, and what was the impact?
What to spotlight:
Systems you’ve built between 2020-2025
Measurable impact (latency cuts, revenue lift, model quality gains)
Specific examples of ownership and leadership
Research skills demonstrated through papers, presentations, or open-source contributions
The resulting profile is more like a curated portfolio than a raw resume. Hiring teams can quickly understand your skills and scope without wading through generic bullet points.
This single profile is reused across multiple Match Days, so you avoid re-entering the same information for each company. Apply once, participate many times.
Step 2: Vetting, Bias-Audited Evaluation, and Fraud Detection
Tech employers increasingly care about authenticity and signal quality. AI-generated resumes, inflated titles, and misrepresented experience create noise that hurts everyone, including legitimate candidates who get lost in the shuffle.
Fonzi AI runs fraud-detection checks to protect both sides: inconsistent employment timelines, mismatched repositories, and suspicious patterns. This isn’t about gatekeeping; it’s about ensuring the candidates who make it through are genuinely qualified for the roles they’re matched with.
Structured evaluation includes:
Skills matrices aligned to specific role types
Coding samples and project narratives were reviewed for substance
Bias-audited models that flag candidates to human reviewers, not auto-reject
Evaluation of work complexity, explanation clarity, and alignment with AI/ML/infrastructure roles
We explicitly avoid relying on proxies like school pedigree alone. The goal is surfacing under-the-radar talent and making signal legible to busy hiring managers who don’t have time to dig through hundreds of applications.
Step 3: Match Day Itself: 48 Hours of Curated Intros
Over a 48-hour window, selected AI startups and growth-stage companies review the vetted candidate pool and send interview requests with salary ranges attached. This is where the efficiency gains become real.
What candidates see:
Which companies are serious about hiring now
What roles they’re opening
Compensation bands upfront, before any time is invested
Fonzi AI acts as a concierge recruiter, helping schedule interviews, resolve conflicts, and ensure both sides move quickly. The experience is intentionally high-signal: a smaller number of curated interviews instead of dozens of low-intent screens.
Example scenario: A senior LLM engineer participates in Match Day on Tuesday. By Wednesday afternoon, she has three interview requests from AI startups, each with salary ranges in her target band. One week later, she has an offer. Total time from Match Day to offer: eight days.
This is what happens when you remove the friction and misalignment from traditional hiring.
Step 4: From Interviews to Offers: Faster, Clearer, Fairer
Post-Match Day interviews typically proceed in a condensed timeline, a few rounds across one to two weeks instead of months. Companies are nudged toward timely decisions and clear feedback, given their upfront commitment to the event.
Fonzi AI supports candidates throughout:
Prep guidance tailored to specific roles and companies
Expectations about interview formats (coding, system design, ML case studies, behavioral rounds)
Negotiation coaching when offers come through
Base salary transparency from the start means fewer surprises. Offers are anchored in ranges shared before first contact, eliminating the “what’s your expected salary?” dance that wastes everyone’s time.
This structured flow lets both sides focus on evaluating mutual fit and skills rather than navigating logistics and ambiguity. The result is a future-proof approach to job searching that respects your time and expertise.
What Tech Employers Want from AI, ML, Infra, and LLM Engineers Specifically
AI and ML roles in 2025 have moved beyond “just train models.” Companies expect end-to-end product and systems ownership. If you’re interviewing for these roles, you need to understand the extra skills employers look for compared to traditional software positions.
Many Fonzi AI partner companies are AI-native startups, so their expectations around experimentation velocity and technical product impact are especially sharp. This section will help you audit your own skill gaps and plan specific upskilling before your next job search or Match Day.
Beyond Coding: What Senior Engineers Are Evaluated On
Many employers look for capabilities that extend far beyond writing code:
Architecture design: Can you design systems that scale, not just implement them?
Stakeholder communication: Can you explain technical decisions to product, business, and executive audiences?
Roadmap influence: Can you shape what gets built, not just how it’s built?
Cross-team alignment: Can you coordinate with research, data, infrastructure, and product teams?
At Staff-level and beyond, employers care about how you choose what to build, not just how you implement it. Decision-making skills become as important as technical execution.
Preparation advice: Document 2-3 examples where you shaped a technical or product direction, deciding on a retrieval strategy for an LLM product, proposing a new evaluation framework, or killing a project that wasn’t working.
Fonzi AI’s profiles and interviews are structured to surface this kind of impact, giving senior engineers space to go beyond algorithm questions.
AI and ML Employers vs. Traditional Software: Extra Skill Layers
AI and ML employers look for teachable abilities in software engineering, plus additional competencies:
Foundational skills:
Statistics and probability theory
Optimization algorithms and numerical methods
Experimentation design and hypothesis testing
Data lifecycle skills:
Labeling strategies and data quality management
Monitoring drift and model degradation
Closing the loop between real-world feedback and model updates
Production ML competencies:
Feature stores and feature engineering at scale
Model deployment and serving infrastructure
Monitoring and observability for ML systems
Alignment with AI infrastructure and security requirements
Empirical rigor:
Running A/B tests or offline evaluations with clear metrics
Understanding statistical significance and practical significance
Avoiding “it seemed better on a small sample” reasoning
Interview preparation: Bring 1-2 concrete case studies where you improved a model metric or product KPI (uplift in CTR, lower false positives, improved latency). Be prepared to explain the tradeoffs you navigated.
LLM and GenAI Roles: Product Sense Meets Prompting and Tooling
LLM-focused roles have surged since 2023, shifting from toy demos to revenue-critical applications by 2025. These roles require a unique combination of skills:
Technical LLM skills:
Prompt design and optimization
Retrieval-augmented generation (RAG) architectures
Evaluation strategies for generative outputs
Safety, guardrails, and responsible AI considerations
Product sense:
Understanding user problems deeply
Navigating latency/quality tradeoffs
Knowing when an LLM is the right tool versus a simpler deterministic system
Cultural competence when building AI for diverse user bases
Example projects to highlight:
Shipping an internal copilot tool with measurable productivity gains
Building an LLM-powered search feature with clear engagement metrics
Designing a multi-step agent workflow with an evaluation framework
Many Fonzi AI employers explicitly search for LLM experience. Match Day slots often include roles like “LLM Infrastructure Engineer,” “GenAI Product Engineer,” or “Applied ML Engineer - LLMs.”
Infra and Platform Engineers: Reliability, Cost, and Scale
Infrastructure and platform roles are critical in AI startups, where GPU costs, latency, and reliability directly impact margins and user experience.
Key skills for infrastructure engineers:
Distributed systems design at scale
Observability (metrics, tracing, logging)
CI/CD for ML systems
Cost optimization across cloud providers
Valuable achievements to showcase:
“Reduced cloud spend by 35% in 2024 through reserved instance optimization and workload scheduling”
“Improved p95 latency for inference service from 450ms to 180ms through caching and batching improvements”
Top employers want infrastructure engineers who can partner with ML teams, not just manage servers. Strong collaboration skills and basic ML literacy are a plus, so understanding what your ML colleagues need makes you a more valuable teammate.
Fonzi AI actively categorizes candidates by these strengths so the right hiring managers see them quickly during Match Day.
Big Tech vs. Startups: Different Emphasis, Same Core Skills
While Big Tech (FAANG-style) and startups both care about fundamentals, they dial different skills up or down based on their context.
Big Tech typically emphasizes:
Depth in narrow technical areas
Process navigation and working within established systems
Long-term maintainability and scale
Extensive testing and documentation
Startups typically prioritize:
Versatility across the stack
Speed and iteration velocity
Comfort with ambiguity and incomplete requirements
Ownership of end-to-end problems
Specific examples:
A FAANG ML role may focus on a large-scale experimentation infrastructure serving millions of users
A seed-stage AI startup may need someone who can do product thinking, model training, and basic DevOps in the same week
Tailoring your approach: Highlight stability and long-term impact for Big Tech applications. Highlight scrappiness, breadth, and rapid learning for startup roles.
Fonzi AI’s marketplace is skewed toward AI-first startups and high-growth companies. The platform helps candidates match with environments aligned to their working style and career goals.
How to Demonstrate These Skills in Resumes, Profiles, and Interviews
Knowing what employers value is only half the battle. The other half is signaling those skills clearly and credibly. This section provides a checklist for preparing your materials and your interview game.
Fonzi AI encourages a portfolio-style approach: concrete projects, metrics, and artifacts rather than buzzwords. Digital communication of your capabilities matters; your online presence and application materials are often the first impression.
Resumes and Online Profiles: Make Your Skills Legible
Keep resumes focused on high-signal content:
Structure:
1-2 pages maximum
Quantified impact for each role since 2018, prioritizing the last 3-4 years
Clear progression and ownership narrative
Skills section: Split into categories for scannability:
Languages (Python, Go, Rust)
Frameworks (PyTorch, TensorFlow, FastAPI)
ML/LLM (transformers, RAG, fine-tuning)
Infrastructure/Cloud (AWS, GCP, Kubernetes)
Data Tools (Spark, dbt, Airflow)
Translating work into metrics:
❌ “Worked on NLP”
✅ “Improved LLM answer accuracy by 7% on internal benchmark in Q3 2024”
Artifacts to link:
GitHub repositories with meaningful contributions
Papers, talks, or technical AI blog posts
Demo videos or project writeups
Fonzi AI profiles should mirror this clarity, no fluff, just concise summaries of projects, tech stacks, and outcomes that let hiring managers quickly assess fit.
Storytelling: Turning Skills into Memorable Examples
Employers remember stories more than lists. Each key skill (leadership, problem solving, adaptability) should have at least one supporting project narrative.
Story structure (STAR method):
Situation: Brief context
Task: What you needed to accomplish
Actions: What you specifically did
Results: Measurable outcomes
Example: “In Q2 2024, our LLM-powered search feature was returning irrelevant results for 23% of queries [Situation]. I was tasked with diagnosing the issue and improving relevance [Task]. I analyzed query logs, identified a gap in our retrieval pipeline for long-tail queries, and implemented a hybrid search approach combining dense and sparse retrieval [Actions]. We reduced irrelevant results to 8% and saw a 15% increase in user engagement with search results [Results].”
Preparation advice:
Prepare multiple versions tailored to technical versus non-technical audiences
Incorporate these stories into cover letters, LinkedIn summaries, and Fonzi AI profile blurbs
Practice aloud until answers sound natural and time-bounded (2-4 minutes per story)
Interview Prep for AI, ML, and Infra Roles
Typical interview components for these roles in 2025:
Round Type | What It Tests | Prep Focus |
Coding | Implementation ability, Python/backend fluency | Data structures, complexity, clean code |
System Design | Architecture thinking, scale considerations | Distributed systems, tradeoffs, ML infra |
ML/LLM Case Studies | Applied ML judgment, experimentation design | Metrics, evaluation, real-world constraints |
Behavioral | Communication, collaboration, leadership | STAR stories, specific examples |
Preparation strategy:
Refresh fundamentals (data structures, complexity, probability, linear algebra where relevant)
Focus on realistic, senior-level problem types rather than trick puzzles
Build a small “interview portfolio” of 4-6 flagship projects you can discuss at multiple depths
Fonzi AI shares role-specific expectations and prep pointers with candidates before Match Day interviews to reduce uncertainty.
Don’t forget to prepare questions of your own. Strong employers expect thoughtful curiosity about:
Data quality and labeling processes
Evaluation and experimentation frameworks
Technical roadmap and team structure
Workplace culture and how decisions get made
Your questions signal as much about your expertise as your answers do. Asking about online courses for upskilling or how the team stays current with digital technology trends shows commitment to growth.
Conclusion
In 2025, tech employers aren’t just hiring for tool familiarity; they’re hiring for outcomes. The job description may list twenty requirements, but decisions ultimately hinge on whether you can solve meaningful problems, collaborate effectively, and scale your impact as the company grows. For recruiters and AI leaders, the strongest candidates combine great technical skill with communication, learning agility, and a track record of driving real-world results.
AI can either muddy this process or make it clearer. When used responsibly, it sharpens signal, audits bias, and supports human judgment; when misused, it becomes an opaque filter that reduces engineers to keywords. Fonzi AI takes the first approach. As a bias-audited, salary-transparent marketplace built specifically for AI, ML, LLM, and infrastructure talent, Fonzi’s 48-hour Match Day connects high-signal engineers with committed employers, fast. The goal isn’t to replace human decision-making, but to give recruiters and hiring managers more space to evaluate what truly matters: your impact, your trajectory, and where you’ll do your best work.
FAQ
What skills are tech employers looking for in senior engineers beyond coding ability?
What’s the difference between skills listed in job descriptions and what actually gets you hired?
Which soft skills do employers prioritize when hiring engineers at FAANG companies?
What skills are AI and ML employers looking for that traditional software roles don’t require?
How do skills employers look for differ between Big Tech and startups?
