How to Get a Job in Tech With No Experience
By
Liz Fujiwara
•
In 2026, many senior developers, data scientists, and infrastructure engineers will find themselves classified as junior candidates when pivoting into applied AI or LLM-focused roles. Job descriptions in AI and machine learning routinely list years of niche experience, recent publications, or production-scale deployments that many strong engineers do not yet have. This article focuses on experienced practitioners who lack direct job titles in AI or ML, not on fresh graduates, and will show them how to close that experience gap efficiently.
Key Takeaways
Senior engineers can still be experience constrained when pivoting into AI roles, even after years in adjacent domains.
Modern AI hiring relies heavily on structured evaluation, so candidates without direct experience must demonstrate ability through targeted projects, public artifacts like code and writing, and focused participation in AI communities.
Curated, match-based hiring channels such as Fonzi can help surface relevant opportunities faster, but success ultimately depends on clear technical signals and human evaluation of demonstrated impact rather than job titles.
Understanding How AI Hiring Works in 2026 Through 2026
Hiring for AI, ML, and infrastructure roles now blends traditional methods with AI-based screening, internal referrals, and curated marketplaces. Tech companies increasingly use large language models and ranking systems to scan resumes, GitHub repositories, and public profiles for specific signals such as frameworks, benchmarks, and deployment experience.
Jobs in computer and information technology are expanding rapidly, which creates both opportunity and competition. Understanding how these pipelines filter candidates helps you reverse engineer what to publish and how to present your work.
Channel | Volume | Signal-to-Noise | Process Transparency | Direct Hiring Manager Access |
Open Job Boards | Very High | Low | Minimal | Rare |
Internal Referrals | Medium | High | Moderate | Common |
Curated Marketplaces (e.g., Fonzi) | Low to Medium | High | High | Typical |
AI in recruiting tends to filter and prioritize, but final decisions still depend on human evaluation of code, communication, and problem framing. Some companies experiment with more automated shortlisting, but high-quality AI teams typically keep humans in the loop to avoid false negatives and to assess research depth, design judgment, and collaboration style.
Understanding this funnel helps candidates without traditional AI backgrounds reverse engineer what to publish and how to present their work so both AI screeners and humans can quickly see relevant strengths.
How Companies Evaluate Experience Without a Title
For AI and ML roles, hiring managers in 2026 often care more about artifacts and impact than about a specific title such as ML Engineer on the resume. The evaluation dimensions that matter include understanding of modern architectures like Transformers, diffusion models, and retrieval mechanisms, experience shipping systems to production, data management and observability skills, and collaboration with product and research teams.
Many teams now assess candidates via GitHub activity, arXiv or conference submissions, Kaggle competitions, open source projects, and technical blog posts. This is especially true when formal job history is thin in the target domain. Employers in the tech industry increasingly prioritize demonstrated skills and applied experience alongside traditional career signals.
A candidate who has never held the title LLM Engineer but has shipped a retrieval-augmented generation system in a backend role will often be treated as having relevant real-world experience by thoughtful hiring managers. The rest of this article focuses on manufacturing this kind of verifiable experience while still in a prior role or during a transition period.
Designing Targeted Projects That Substitute for Job Experience
For AI and ML roles, a small number of well-chosen, production-quality projects created in 2026 can be more convincing than a long list of smaller experiments or tutorials. Creating personal projects, such as developing a simple application or analyzing a dataset, can help demonstrate your skills and strengthen your portfolio when applying for tech jobs.
Projects should mirror real company problems, use up-to-date tools such as OpenAI GPT-4, Claude, Llama 3, PyTorch 2.x, Ray, or vector databases like Pinecone and Weaviate, and demonstrate end-to-end thinking from data ingestion to evaluation and monitoring. Building relevant skills for tech jobs is also supported by online learning platforms offering structured courses in coding and data analysis.
High-quality documentation and a clear README are part of the experience signal. A project without explanation is rarely enough to convince a reviewer of your depth of understanding.
Project Ideas for AI and LLM Engineers
AI and LLM engineers need to show they can go from API experimentation to structured, reliable systems that real users can depend on. The following project types send strong signals to reviewers evaluating software engineering and machine learning capability.
Build a retrieval-augmented generation service around a real dataset, such as financial filings or technical documentation. Highlight embedding choices, chunking strategy, and latency tradeoffs.
Create an evaluation harness for LLM outputs using automated metrics like BLEU or BERTScore paired with human evaluation. This approximates how production teams evaluate models.
Build a small multi-agent system for workflows such as support triage or code review, emphasizing logging, failure handling, and cost tracking. Contribute to open source tooling by improving performance, tests, or documentation.
Project Ideas for ML Researchers and Applied Scientists
ML researchers are evaluated on experimentation quality, interpretation of results, and communication rather than simply applying models. Strong communication and problem-solving skills are often valued alongside technical depth.
Reproduce a recent paper with clear experiment logs, ablations, and baseline comparisons. This demonstrates research rigor.
Evaluate instruction-tuning approaches on open models like Llama 3, including error analysis and robustness considerations. Build small research projects exploring tradeoffs such as context length vs retrieval performance or quantization vs accuracy, and present results clearly.
Project Ideas for Infra, Platform, and MLOps Engineers
Infrastructure and MLOps engineers focus on reliability, observability, and cost control. Real-world or simulated production experience is key.
Build a containerized inference system with metrics, tracing, and structured logging. Implement a CI/CD pipeline for ML workflows including training, validation, and deployment with rollback strategies. Create or document a feature store or experiment tracking system using tools like MLflow or Weights & Biases.
Include benchmarks such as throughput and p95 latency to demonstrate production readiness.
Turning Side Projects Into a Credible AI Portfolio
Hiring managers in 2026 prefer curated portfolios over scattered experiments. Organize three to five flagship projects tied to specific roles like LLM Engineer or Applied Scientist, and present them clearly through GitHub, a personal site, or a PDF portfolio.
Include architecture diagrams, usage instructions, and notes on future improvements. Interactive demos via Hugging Face Spaces, Kaggle, or Colab can further strengthen credibility.
A curated marketplace like Fonzi can surface well-structured portfolios directly to AI-focused companies, making this kind of focused project work especially high leverage.
Structuring Your Portfolio for Different AI Roles
AI engineers, ML researchers, and infra and platform engineers should prioritize different artifacts for each role type. Create separate sections or pages in the portfolio, each labeled for a target role and ordered so the most relevant projects appear first, reducing cognitive load for reviewers.
For AI engineer roles, the portfolio should emphasize end-to-end systems, benchmarks, and evidence of iteration. For research roles, highlight experimental rigor, literature grounding, and detailed write-ups. Staying current with industry trends and skills can help you demonstrate adaptability, which is a core skill for tech professionals.
For infra and MLOps roles, show config files, infrastructure-as-code snippets, deployment topologies, and reliability metrics while omitting sensitive credentials or proprietary details. Include contributions to other projects, such as merged pull requests in major libraries, with short contextual descriptions rather than raw links so reviewers can understand impact quickly. Using specific keywords from the job description is essential for passing Applicant Tracking Systems when your resume is screened.
Navigating AI-Focused Hiring Channels Without a Traditional Background
In 2026, senior candidates shifting into AI-heavy roles must be deliberate about where they apply and how they are discovered. Generic job boards tend to drown strong but nonstandard profiles in volume, making the job market more challenging for career changers. Networking is also crucial.
The main channels include direct applications on company career pages, networking via conferences and online communities, referrals from ex-colleagues, and structured channels such as curated marketplaces. Networking and community involvement are crucial in the tech industry, as they can lead to job opportunities and insights into the field. Informational interviews can lead to connections and potential referrals in your desired field.
Engage in specialized communities such as ML conference Slack groups, open source project maintainers, or focused Discord servers for LLM practitioners to find unlisted roles and get warm introductions. Connecting with professionals in the field through networking events, tech meetups, or online platforms like LinkedIn can provide insights into the industry and potential job offers. Leveraging and growing your network should not only be about getting a recommendation; it can also provide mentorship, guidance, and insights into job openings.
Regardless of channel, candidates without direct job experience in AI must route reviewers toward their portfolio and concrete outcomes as early in the conversation as possible.
Using Structured and Curated Hiring Models Effectively
Structured models, including internal hiring pipelines and external curated marketplaces, reduce randomness by aligning evaluation criteria around specific skills and outputs. Small businesses may have more flexible hiring criteria than large corporations, making them good targets for career change efforts.
When using a marketplace such as Fonzi, keep profiles technically detailed, including links to repos, benchmarks, and short summaries of relevant systems you have built, to help talent partners understand where you fit best. Approach these channels as collaborations rather than passive listings by updating progress, adding new projects, and clarifying role preferences such as research-heavy or production-focused.
These models typically involve humans who understand both engineering and business needs, which is particularly useful for candidates whose prior titles do not match current AI job labels but whose tech skills are highly relevant. Even in structured systems, interviews remain conversations between people, so clarity, humility, and precise communication still matter as much as technical artifacts.
Preparing for AI and ML Interviews Without Direct Experience
AI and ML interviews in 2026 combine traditional coding, systems design, and domain-specific deep dives into model behavior, data choices, and tradeoffs. Preparation requires three pillars: fundamentals review covering math, algorithms, probability, and systems; modern AI tooling fluency; and communication of past work in a way that substitutes for official experience.
Candidates can map existing work into AI-relevant narratives. A previous data pipeline project becomes a discussion about data quality, feature engineering, and monitoring that matches ML expectations. Soft skills are essential in tech roles as they transform a good tech employee into a great one, emphasizing the importance of communication, teamwork, and problem-solving abilities.
Candidates should focus on developing skills through academic work, extracurricular activities, or non-traditional experiences to bolster their resumes. Run mock interviews with peers, use public question banks for ML and LLM roles, and rehearse explanations of key projects from problem statement to constraints and post-deployment learnings. Practice both with and without AI-generated coding assistance, since some companies restrict usage in interviews while others explicitly allow it.
Structured evaluation in many AI teams values clear tradeoff reasoning, such as when to fine tune versus when to rely on prompt engineering. You can stand out by articulating such decisions even without formal role history.
Common Interview Patterns for AI, ML, and Infra Roles
Candidates should expect live coding, system and model design, research or deep technical discussions, and behavioral alignment rounds. Highlight transferable skills such as communication, problem-solving, and teamwork which apply to any role.
For AI engineer interviews, typical topics include tokenization, attention mechanisms, context windows, prompt strategies, evaluation metrics, and latency or cost optimization in LLM pipelines. ML research or applied scientist interviews focus on problem formulation, experiment design, statistical significance, regularization strategies, and familiarity with current literature in subfields like vision, language, or reinforcement learning.
Infra and platform engineers can expect questions about distributed systems, container orchestration, GPU scheduling, model serving strategies, and handling versioning for models and data. Employers value coachability, enthusiasm, and a strong work ethic, especially when hiring for entry level positions.
Lead with real projects and measurable outcomes during answers, framing responses around context, actions, and results instead of generic responsibilities. Emphasizing your soft skills, such as strong communication skills and problem solving skills, is crucial when applying for tech jobs, as these skills are highly valued in collaborative environments.
Using Your Portfolio as the Anchor in Interviews
Candidates without direct job titles in AI should steer interviews toward their strongest artifacts, because these are the clearest proof of capability. Securing a job without prior experience requires a shift in how you present your background, moving from a list of past employers to a demonstration of your potential and transferable skills.
Prepare concise, technically specific walkthroughs of two or three flagship projects, including design choices, tradeoffs, and what failed along the way, to demonstrate depth of ownership. Reference concrete metrics and outcomes such as latency improvements, cost reductions, or accuracy gains instead of vague claims like significantly faster or much better. Highlighting transferable skills on your resume can demonstrate to hiring managers that you possess relevant industry skills, even if you lack direct experience in the tech field.
Learn to use a virtual whiteboard or shared doc effectively for diagrams and pseudo-code, since many AI and infra interviews in 2026 are remote work by default. Being explicit about unknowns, and how you would investigate them, signals maturity and research mindset, which can offset lack of prior job titles in the domain.
Conclusion
In 2026, AI hiring rewards candidates who generate clear, public signals about their abilities, even if their resume does not yet show traditional AI roles. Targeted engineering projects, a coherent portfolio, thoughtful use of hiring channels, and disciplined interview preparation can collectively substitute for years of official experience and help you get a job with no experience in the specific domain.
Choose one concrete project from this article, plan a two-month build and evaluation sprint, and then update your public profiles or curated marketplace application to reflect the new work. Your next tech career opportunity depends on what you build next, not what title you held before.
FAQ
How do I get a job in tech if I have no professional experience?
What entry level tech roles are the most realistic to break into with no background?
How do I land a remote tech job when I have no experience to point to?
What should I build, learn, or do to make up for a lack of work experience?
Do I need a degree or certification to get hired in tech without experience?
