Most organizations are not short on AI enthusiasm. They are short on engineers who can build AI systems that work reliably in production at enterprise scale. Focus GTS places senior AI engineers who design LLM integrations, RAG pipelines, agentic workflows, and AI-native product features that go beyond demos into dependable shipped software. 500+ technology specialists placed at Fortune 100 companies since 2018. Screened resumes in 48 hours.
What this role builds
The gap between building a proof-of-concept with an LLM API and building an AI system that works reliably in enterprise production is wider than most hiring managers expect. This page is about engineers who have crossed that gap.
LLM integration at enterprise scale is not just API calls. A senior AI engineer designs the prompt engineering strategy -- system prompt architecture, few-shot example selection, chain-of-thought scaffolding, and output parsing -- in a way that is testable, versionable, and improvable over time. They also design the evaluation framework that tells the team whether a model change improved or degraded output quality, which is the capability that distinguishes organizations that can iterate on AI features from those that deploy once and hope for the best.
Retrieval-Augmented Generation is the primary pattern for grounding LLM outputs in enterprise knowledge. Building a RAG pipeline that actually works at scale requires engineering judgment at every stage: document chunking strategy (fixed-size vs. semantic vs. structural), embedding model selection, vector database architecture and approximate nearest neighbor index configuration, retrieval ranking and reranking, hybrid search that combines dense and sparse retrieval, and context window management for long documents. Engineers who have iterated on RAG pipelines in production have developed opinions about what the benchmarks miss and what actually moves output quality in real use cases.
Agentic AI systems -- where LLMs plan and execute multi-step tasks using tools and other models -- require a distinct engineering discipline. Senior AI engineers who have shipped agentic workflows understand how to structure tool definitions that LLMs can reliably use, how to design fallback and retry logic for the non-deterministic execution paths that agents take, how to build observability into agent traces so failures are diagnosable, and how to constrain agent behavior within safety and compliance boundaries that enterprise risk teams will accept. Building agents that work in demos is straightforward. Building agents that work reliably in production on real enterprise data is the problem that requires senior engineering judgment.
AI feature integration into existing products -- embedding AI-powered search, recommendations, document summarization, or conversational interfaces into enterprise software -- requires engineers who understand both LLM systems and production software engineering: latency budgets, caching strategies, fallback behaviors, cost monitoring, and the A/B testing infrastructure that lets the team validate that AI features are actually improving user outcomes rather than just adding novelty.
The hiring challenge
The AI engineering talent market in 2025 and 2026 is characterized by high demand, credential inflation, and a genuine scarcity of engineers with production track records.
The rapid commoditization of LLM access has produced a large population of engineers who have built AI projects but a small population who have built AI systems that work reliably at enterprise scale. The credentials that might signal AI engineering depth -- GitHub projects, conference talks, published models -- are increasingly easy to produce without having shipped anything real. The interview processes that actually distinguish production experience from demo experience require interviewers who have built production AI systems themselves, which rules out most generalist engineering managers.
The pace of model and tooling evolution has created an additional signal problem. An engineer who was genuinely senior at LLM integration twelve months ago may be working with a skill set that is partially obsolete -- the tooling, the model architectures, and the best practices for RAG and agentic systems have moved significantly. Seniority in AI engineering right now means ongoing engagement with what is currently working in production, not a static credential earned years ago.
Focus GTS has been placing AI specialists at enterprise organizations since before the current wave of LLM adoption. We know what genuine senior AI engineering depth looks like -- specifically, what candidates have actually shipped in production, what evaluation frameworks they used to measure system quality, and what failure modes they encountered and solved. We do not run keyword searches against "Python" and "LangChain." We assess engineering judgment about AI system design.
Tell us the AI system you need built: RAG pipeline, LLM feature integration, agentic workflow, fine-tuning program, or AI product from scratch. Screened resumes in 48 hours of intake.
Start the ConversationScreened resumes in 48 hours. Fortune 100 track record since 2018.
Our screening process
We ask about specific AI systems shipped in production -- the architecture choices made, the evaluation framework used to measure quality, and the failure modes encountered and resolved at scale.
Assessed on chunking strategy decisions, embedding model selection rationale, vector database architecture, hybrid search design, and quantitative evaluation methodology for retrieval quality.
Screened for genuine agentic workflow engineering: tool definition design, non-deterministic execution handling, observability and tracing implementation, and constraint design for enterprise compliance requirements.
Validated on production engineering practices: latency optimization, caching strategy, model cost monitoring, A/B testing infrastructure for AI features, and fallback behaviors for model failures.
Engagement options
Frequently asked
A senior AI engineer designs AI systems from first principles -- not just by calling an LLM API and returning the output. They understand how to evaluate model outputs for quality and consistency, how to design retrieval-augmented generation pipelines that actually improve accuracy, when fine-tuning produces better outcomes than prompt engineering, how to build observable AI systems with tracing and evaluation frameworks, and how to design agentic workflows that remain reliable when composed actions fail. They have shipped AI features in production and dealt with the failure modes that do not appear in demos.
Yes. RAG is the dominant architecture pattern for grounding LLM outputs in enterprise knowledge bases. Senior AI engineers with RAG depth have designed the full pipeline: document chunking strategies, embedding model selection, vector database architecture and index configuration, retrieval ranking and reranking, context window management, and the evaluation framework that measures whether retrieval is actually improving answer quality versus naive generation.
Yes. Enterprise AI programs increasingly require engineering judgment about which model to use for which task -- frontier commercial models for reasoning-intensive tasks, smaller open-source models for cost-sensitive or latency-sensitive inference, and fine-tuned models for domain-specific classification or generation tasks. We place AI engineers who have worked across this model landscape in production.
Our AI engineers have production experience across the primary tooling landscape: LangChain, LlamaIndex, and direct SDK use for orchestration; Pinecone, Weaviate, pgvector, and Qdrant for vector storage; LangSmith, Weights and Biases, and Arize for evaluation and observability; and Python-based ML frameworks including PyTorch and Hugging Face Transformers for fine-tuning and custom model work. We screen for depth in the specific tooling stack your program requires.
Yes. All candidates Focus GTS actively presents for senior AI engineer roles are US-based. Enterprise AI programs that involve proprietary data, compliance requirements, or security-sensitive use cases frequently require US-based engineering teams, and we apply this filter before submission.
Screened resumes in 48 hours. Tell us the AI system you need built and what your current engineering team looks like.
