Match Your Skills to Jobs: Skills-Based Career Search Tools & Strategy
By
Ethan Fahey
•
Dec 30, 2025
It’s 2026, and for many AI engineers, job hunting feels backwards. Your inbox is full of generic recruiter messages for roles that barely resemble your actual skill set, while the jobs you’d actually be excited about are buried under vague titles like “Machine Learning Engineer” or “Data Scientist.” At the same time, teams are looking for very specific capabilities: people who can ship RAG pipelines, scale GPU inference, or build robust evaluation frameworks for LLMs, but traditional hiring still leans on titles and blunt years-of-experience filters that miss the mark.
That’s why hiring by skills is becoming the default for serious AI teams. Instead of guessing based on job titles, a skills-first approach focuses on what you’ve built, the systems you’ve shipped, and the problems you’ve solved. Platforms like Fonzi AI are built around this idea, using structured, skills-based matching to connect AI engineers, ML researchers, infra engineers, and LLM specialists with roles that actually need their expertise. For recruiters, it means higher signal and faster hiring; for engineers, it means fewer irrelevant conversations and more opportunities that align with real work.
Key Takeaways
Modern AI hiring is shifting from generic job titles and resume keywords toward validated skills, portfolios, and project outcomes, especially for roles in LLMs, ML research, and AI infrastructure.
Fonzi is a curated talent marketplace built specifically for AI engineers, ML researchers, infra engineers, and LLM specialists, using AI to match talent by skills rather than buzzwords.
Fonzi’s Match Day creates a focused, time-bound window where vetted companies reach out directly to candidates based on skills alignment and stated interests.
Skills-based job search means starting with a clear inventory of what you can build, scale, and ship, then using platforms that map those capabilities to roles instead of relying on vague title matching.
Fonzi uses AI responsibly to reduce bias and noise in hiring while keeping humans at the center of every decision.
From Job Titles to Skills: How AI Hiring Is Changing
The old labels are breaking down. “Software Engineer” and “Data Scientist” once covered a reasonable range of work, but in today’s AI industry, these titles are nearly meaningless without context. Companies now hire for precise skill profiles: LLM Ops, Evaluation Engineer, Infra Engineer for distributed training, or Applied Scientist specializing in RLHF.
Since around 2022, organizations building AI products have increasingly defined roles by technology stacks, modeling techniques, and deployment environments rather than generic titles. A job listing might specify experience with PyTorch, transformer architectures, Ray for distributed training, and production serving on Kubernetes, but still call the role “ML Engineer,” indistinguishable from hundreds of other postings.
Traditional job boards compound this problem. They index primarily by title and years of experience, which can obscure great-fit roles for specialists in areas like retrieval-augmented generation, vector databases, or safety and alignment research. The search experience often feels like typing words into a void and hoping something relevant surfaces.
Skills-based hiring flips this model. Companies specify concrete needs, scaling inference on GPUs, fine-tuning instruction-following models, designing eval suites, and searching for candidates based on verified capabilities rather than assumed ones.
Responsible companies now combine AI tools with structured interviews, work samples, and portfolio reviews, rather than relying purely on resume keyword filters.
This shift means that if you can articulate exactly what you build and how you build it, you’re already ahead of candidates still relying on title-matching strategies.
Mapping Your AI Skills to High-Impact Roles
Before using any skills matcher or platform, you need a clear inventory of your technical and domain skills. This isn’t just resume polishing; it’s the foundation for finding careers that genuinely match what you’re capable of doing.
For AI engineers, ML researchers, infra engineers, and LLM specialists, marketable skills fall into several categories:
Model development: PyTorch, JAX, TensorFlow, transformer architectures, diffusion models, fine-tuning methods (LoRA, PEFT), RLHF
Infrastructure: Kubernetes, Ray, Docker, GPU orchestration, Terraform, cloud platforms (AWS, GCP, Azure)
LLM productization: RAG systems, evaluation frameworks, prompt engineering, latency optimization, production serving
Research: paper implementation, benchmarking, reproducibility, experimental design, literature review
The key is thinking in terms of “I can reliably do X in Y environment with Z constraints” rather than just listing tools. For example: “deploy low-latency inference on T4 and A100 GPUs for production workloads” or “design and run eval suites for instruction-following models with 10K+ test cases.”
Assessing Your Current Skills (Technical, Research, and Product)
Start by writing a detailed skills document grouped into three buckets:
Core engineering skills:
Programming languages (Python, C++, Rust, Go)
Frameworks and libraries (PyTorch, JAX, TensorFlow, Hugging Face)
Infrastructure (Kubernetes, Docker, Ray, Airflow)
Software engineering fundamentals (testing, version control, CI/CD)
ML/LLM depth:
Architectures you’ve worked with (transformers, CNNs, diffusion models)
Training methods (pretraining, fine-tuning, RLHF, LoRA)
Evaluation approaches (benchmarks, human eval, safety testing)
Research areas (generative modeling, alignment, retrieval)
Product skills:
Features shipped to production
Experimentation and A/B testing experience
User impact metrics you’ve influenced
Collaboration with product and design teams
For each bucket, identify concrete artifacts: GitHub repos, arXiv papers, internal tech docs, benchmarks, or demos from projects in the last few years. These artifacts become evidence when you later need to demonstrate your ability to potential employers.
Evaluate your depth honestly. There’s a difference between “can independently design and train custom transformer architectures” and “comfortable fine-tuning open-source LLMs with existing scripts.” Both are valuable, but for different roles.
Finally, document real constraints you’ve solved for: latency targets, dataset sizes, cost budgets, security requirements, or public safety considerations. These details signal that you understand production realities, not just research prototypes.
Identifying Transferable and Adjacent Skills
Many capabilities transfer across AI subfields more easily than job titles suggest.
Familiarity with deep learning optimization, distributed training, and experiment management can transfer from computer vision roles into LLM pretraining or fine-tuning. The core problem-solving approaches, designing training loops, debugging gradient issues, and managing large-scale data pipelines, remain similar even when the domain changes.
Consider these adjacent transitions:
An infra engineer with Kubernetes and Terraform experience can move into AI infra roles supporting training clusters or inference services
A backend engineer strong in distributed systems and networking can transition to ML platform engineering by adding experience with model serving and observability
A researcher in a non-ML field with a strong mathematics and statistics background can retrain into ML research, with the taxonomy showing which skills are missing and which are shared
Research-oriented skills like literature review, reproducibility, and benchmarking transfer well across subfields and into applied research roles. If you’ve published papers or contributed to open-source projects, those activities demonstrate capabilities that matter across occupations.
Explicitly note which of your skills are domain-agnostic. Scalable backend design, experimentation culture, and clear technical communication broaden your viable role set significantly.
Aligning Skills with Specific AI Role Types
Understanding which skills align with which role families can help you focus your search and tailor your positioning.
Role Family | Core Skills | Secondary Skills |
LLM Engineer | Prompt engineering, fine-tuning, RAG, eval design, latency-aware deployment | Alignment, safety, agent frameworks |
ML Researcher | Experimental design, paper implementation, benchmarking, novel architecture development | Production ML, MLOps basics |
ML Platform / MLOps | Kubernetes, model serving, CI/CD for ML, experiment tracking, feature stores | Deep learning fundamentals, cost optimization |
AI Infra Engineer | GPU orchestration, distributed training, cluster management, performance optimization | ML frameworks, data pipelines |
Applied Scientist | End-to-end ML, product experimentation, cross-functional collaboration | Domain expertise, user research |
Evaluation / Safety Engineer | Eval framework design, red teaming, alignment testing, safety benchmarks | LLM internals, policy knowledge |
Tag yourself with 1–2 primary role families and 1–2 secondary options. This keeps your search focused yet flexible, and directly improves fit when using platforms like Fonzi that rely on similar skill and role tags.
Tools for Skills-Based Job Matching (and Where Fonzi Fits)
There are many skills-matcher tools available, some generic, some specialized. Understanding their differences helps you allocate your time to platforms that actually serve AI talent well.
Generic career matchers (including resources linked to the national center for O*NET and similar occupation databases) provide broad industry overviews and can help you discover occupations you might not have considered. They’re useful for understanding how your current skills map to recognized job families across the labor market.
Large job boards with skill filters let you search by keywords and sometimes filter by technology stacks. The volume is high, but so is the noise. For AI roles, these platforms often have coarse-grained skills categories that lag behind current stacks, weak coverage of 2023–2026 LLM tooling like LangChain, vector databases, or RAG frameworks.
Company-specific hiring funnels work well if you already know where you want to work. But discovering new opportunities requires manually checking dozens of sites and company pages.
Fonzi takes a different approach: a curated marketplace focused exclusively on AI and ML roles, where both talent and companies are screened. Instead of quick matches found in search results that barely relate to your expertise, you get opportunities where your specific skills are the reason you’re being considered.
Comparing Skills-Based Job Search Tools
Tool Type | How Matching Works | Strengths for AI Talent | Limitations | Best Use Case |
Generic Job Boards | Keyword search, title filtering, years of experience | High volume, broad coverage | Low signal, keyword stuffing, outdated skill categories | Casting a wide net for general software roles |
Traditional Skills Matchers | Self-assessment → occupation mapping based on standardized taxonomies | Good for career exploration, government-backed data | Often lag behind emerging AI stacks, generic skill descriptions | Understanding broad career paths and similar activities across fields |
Company-Specific Funnels | Direct application, ATS keyword matching | Clear requirements if you know the company | Discovery is manual, no cross-company matching | Targeting specific employers you’ve researched |
Fonzi | Structured skills data + AI-assisted matching + human curation | Purpose-built for AI roles, vetted companies, Match Day efficiency | Smaller marketplace (by design), focused on AI/ML only | High-signal opportunities with companies actively hiring AI talent |
Fonzi uses structured skills data tailored to AI stacks such as PyTorch, JAX, LangChain, Ray, transformer architectures, and more. It pays attention to research backgrounds, signals from real projects and publications, and preferences around work style.
This isn’t just another filter layer. It’s a targeted marketplace with a smaller, higher-quality set of companies and candidates who are serious about making a good fit happen.
How Fonzi Uses AI Responsibly in Matching
You might wonder what’s actually happening when AI gets involved in matching you to roles. Fonzi’s approach is designed to be transparent and fair.
Fonzi uses AI to analyze structured skill profiles, experience, and preferences, not to auto-reject candidates based on superficial signals. The system prioritizes strong potential matches and reduces noise for both sides, while final decisions remain with human recruiters and hiring managers.
Privacy-respecting practices matter here:
No resale of candidate data to third parties
Transparent control over what companies see in your profile
Clear opt-in for Match Day participation
Ability to update preferences and visibility at any time
The goal is to mitigate bias by focusing on actual skills, projects, and outcomes rather than pedigree or network. School logos and employer brand names don’t drive matching; instead, what you can demonstrably do drives matching. AI helps recruiters focus on people; it doesn’t replace them.
Inside Fonzi: A Skills-First Marketplace for AI Talent
Fonzi is a curated marketplace for AI and ML talent that connects candidates with top-tier startups and established technology companies building AI products. Every company on the platform is vetted for serious hiring intent, clear role requirements, and alignment with responsible hiring practices.
The candidate experience is straightforward:
Join and create a skills-forward profile
Document your technical capabilities, projects, and preferences
Participate in Match Days when you’re actively looking
Move quickly into conversations with aligned teams
Fonzi focuses on positions like LLM Engineer, Applied ML Scientist, AI Infra Engineer, MLOps / ML Platform Engineer, and Evaluation / Safety Engineer. These aren’t generic postings; they’re roles at companies that understand what they need and can articulate it clearly.
Creating a High-Signal Skills Profile on Fonzi
Your Fonzi profile is where you translate your skills inventory into something companies can act on. Think of it as a structured portfolio, not a traditional resume.
Sections to emphasize:
Core stacks: Python, PyTorch, JAX, CUDA, TensorFlow
Model experience: Transformers, diffusion models, RLHF, fine-tuning methods
Infra experience: Kubernetes, Ray, Airflow, Docker, cloud platforms
Product outcomes: Latency improvements, cost savings, user impact metrics, throughput gains
Include 2–4 concrete, recent projects with short descriptions covering:
The problem you were solving
Your approach and the tools you used
Measurable results (model uplift, throughput gains, infra cost reduction, improved BLEU/ROUGE scores)
Research contributions matter too. Link to published papers, open-source repos, benchmark leaderboards, or evaluation frameworks you’ve developed. These artifacts provide evidence of depth that generic profiles lack.
Fonzi’s matching system leverages these detailed skills and project tags to surface precise opportunities during Match Day. The more specific you are, the better your matches will be.
How Match Day Works for AI Candidates
Match Day is a recurring, time-boxed event where vetted companies review curated candidate profiles and send interview requests based on skills alignment.
Typical flow:
Complete or update your profile before a scheduled Match Day
Signal your preferences (remote vs. onsite, research-heavy vs. product-heavy, compensation bands)
Receive targeted introductions during the event window
Review and respond to opportunities that interest you
The benefits are real:
Condensed timelines: Days instead of weeks to first conversations
Higher quality: Fewer but more relevant opportunities
Reduced friction: Less back-and-forth compared to cold applications across dozens of job boards
Companies come into Match Day with specific needs, such as an infra engineer for GPU cluster scaling, an LLM engineer for RAG systems, and an evaluation engineer for safety testing, and use Fonzi to quickly identify 5–10 best-fit candidates.
Match Day is opt-in. You control when you participate, what you’re open to, and whether to engage with each opportunity. No surprises, no spam.
How Fonzi Reduces Bias and Protects Candidate Experience
Fonzi’s process is designed around fairness and quality interaction, not volume.
Practices that protect candidates:
Structured profiles emphasize skills and outcomes over school logos or employer brand
Explicit anti-spam policies limit how often and how companies can reach out
Companies must follow guidelines for respectful, informative outreach
Clear role expectations, compensation bands when possible, and realistic timelines shared upfront
Candidates can provide feedback on company interactions, which informs future curation of the marketplace. If employers waste candidate time or misrepresent roles, that data matters.
The broader theme: AI tools surface strong matches so human recruiters and hiring managers can spend more time on real, respectful conversations rather than sorting through hundreds of applications.
Practical Strategy: Running a Skills-Based Job Search in AI
Here’s a step-by-step playbook for finding AI roles by skills rather than titles.
This strategy combines self-assessment (which you’ve now done), targeted platform use (including Fonzi), and deliberate networking with AI-focused teams. It works whether you’re an early-career engineer with a strong open-source skillset or a senior researcher moving from academia or Big Tech.
Treat this as a repeatable cycle: assess your skills, test the market, gather feedback, and refine your positioning based on what you learn.
Searching for Roles by Skills, Not Titles
Stop searching for “ML Engineer” and start searching for what you actually do.
Concrete search patterns that work:
“LLM evaluation engineer”
“RAG systems engineer”
“distributed training PyTorch”
“GPU infra Kubernetes”
“safety and alignment researcher”
Combine skills keywords to uncover niche roles: “Ray”, “Triton”, “LangChain”, “LoRA”, “RLHF”, “OpenAI API”. These terms surface positions that might be buried under mainstream job titles.
On Fonzi, you can rely less on manual keyword searches because the platform already maps your skills to relevant roles. But the same mindset helps you interpret role descriptions and assess fit quickly.
Keep a short list of target problem domains to filter opportunities:
Recommendation systems
Search and retrieval
Content generation
Agentic workflows
Safety and evaluation
This focus prevents you from applying everywhere and getting lost in a sea of irrelevant interviews.
Showcasing Your Skills: Portfolios, Profiles, and Resumes
In skills-based hiring, evidence of ability matters more than polished words on a resume.
Build a focused AI portfolio:
GitHub repos with clean, documented code
Colab notebooks demonstrating specific techniques
Demo videos walking through a system you built
Short case studies highlighting problem → approach → results
Tailor your resume to emphasize concrete accomplishments:
“Cut model inference latency by 40% on A100 GPUs”
“Improved BLEU score by 12% through custom tokenization and data augmentation”
“Reduced infra cost by 35% by migrating to spot instances with graceful checkpoint recovery”
Link to open-source contributions or benchmarks whenever possible. Briefly explain context and impact rather than listing every experiment.
For Fonzi specifically, align your profile content with what AI hiring managers care about: shipped systems, scale, robustness, and research rigor. The administration of your job search is much simpler when your profile does the heavy lifting.
Preparing for AI-Focused Interviews
Interviews for AI roles typically include several components:
Interview Type | What to Expect | How to Prepare |
System Design | Design ML/LLM systems (training pipelines, serving infra, RAG architecture) | Practice end-to-end designs, understand trade-offs (latency vs. cost, accuracy vs. speed) |
Coding | Algorithms, data structures, sometimes ML-specific code | LeetCode-style practice, implement training loops and data processing from scratch |
ML Theory | Model architecture questions, optimization, evaluation strategies | Review transformer internals, attention scaling, training vs. inference trade-offs |
Project Deep Dive | Walk through past work in detail, explain decisions and failures | Prepare 2–3 detailed walkthroughs with problem framing, trade-offs, and learnings |
Concrete preparation topics for LLM roles:
Transformer internals and attention mechanisms
Training vs. inference trade-offs
Evaluation strategies for generative models
Handling hallucinations and safety concerns
Distributed training architectures
Fonzi’s companies often share clear interview expectations ahead of time, allowing you to focus on preparation instead of guessing what they’ll ask.
Using AI to Put Skills and People First
The strongest AI job searches in 2026 are increasingly skills-based. Instead of relying on job titles that vary wildly from company to company, they focus on what you can actually build, scale, and ship, whether that’s production LLM systems, ML infrastructure, or end-to-end applied models. When used responsibly, AI in hiring helps cut through the noise by surfacing real matches and reducing bias, so recruiters and hiring managers can spend less time filtering resumes and more time having meaningful technical conversations.
That’s where platforms like Fonzi come in. Fonzi is built around skills-first profiles, curated companies, and transparent AI matching, making it easier for AI engineers and teams to find each other without the keyword games. With high-signal Match Days and a focus on real-world capability over titles, Fonzi helps candidates land roles where their skills are actually used, and helps companies hire faster and more confidently. A well-run skills-based search doesn’t just land you a job; it connects you to the right environment to keep growing and building meaningful AI products.
