AI Engineer
Specialization Paths

RAG (Retrieval-Augmented Generation) is one of the hottest specializations. You build systems that combine search with LLMs—document Q&A, knowledge bases, enterprise search. Skills: vector databases, embedding models, chunking strategies, hybrid search, evaluation. Demand: Very high—every company wants to make their documents searchable with AI. Compensation: Premium pay, especially for production-proven experience. Risk: RAG might get simplified by better models, but the patterns will evolve, not disappear. Good choice if you like search systems and data architecture.

Voice AI specialists build speech-enabled applications—voice assistants, AI phone systems, conversational voice agents. Skills: speech-to-text, text-to-speech, real-time streaming, telephony integration, latency optimization. Demand: Growing fast—AI call centers, voice assistants, accessibility. Compensation: Strong pay, especially for production voice agent experience. Risk: Platform companies might commoditize voice, but custom solutions will always be needed. Good choice if you like real-time systems and audio.

Agent specialists build autonomous AI systems that take actions—tool-using LLMs, multi-step reasoning, agentic workflows. Skills: function calling, orchestration, planning algorithms, error recovery, security. Demand: Rapidly increasing as companies move beyond simple chatbots to agents that can actually do things. Compensation: Premium pay for working agent systems. Risk: Agents are still evolving—patterns may change significantly. Good choice if you like complex systems and autonomy problems.

MLOps specialists build the infrastructure for ML systems—deployment, monitoring, pipelines, experiment tracking. Skills: Kubernetes, model serving, CI/CD for ML, data pipelines, monitoring/observability. Demand: Steady—every company with ML needs MLOps. Compensation: Strong, especially at large companies with scale. Risk: Cloud platforms keep adding MLOps features, but custom work remains needed. Good choice if you like infrastructure and DevOps.

Platform specialists build internal AI platforms—self-service systems for data scientists and AI engineers. Skills: Kubernetes, GPU orchestration, developer experience, API design, observability. Demand: High at large companies building internal platforms. Compensation: Strong—combines platform engineering with AI domain knowledge. Risk: Only relevant at companies large enough to need platforms. Good choice if you like building tools for other engineers.

Consider: 1) Interest—you'll need sustained curiosity to go deep. 2) Aptitude—play to your strengths. 3) Market demand—research what companies are hiring for. 4) Competition—some specializations are more crowded. 5) Future trajectory—is this growing or shrinking? Start by building projects in 2-3 areas, then double down on what resonates. Don't specialize too early—get breadth first, then depth. T-shaped skills (deep in one, broad across many) are ideal.

Build broad AI skills first: 2-3 years covering LLM basics, RAG, agents, and deployment. Then start leaning into a specialization. Too early and you might pick wrong; too late and you're competing with specialists who have more depth. Most successful specialists have 4-6 years experience when they fully commit to a domain. You can be a 'specializing generalist' along the way—known for one thing but capable across the board.

Building depth: 12-18 months of focused work in a domain. Building reputation: 2-3 years of shipping projects, creating content, and networking in the space. Being sought after: 3-5 years of proven production experience. The timeline accelerates if you work at companies known for that specialization, contribute to open source, or create content. Specialization is a long game—expect 3-5 years to fully establish yourself.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.