ABOUT:
Builds generative AI applications — LLM-powered features, RAG pipelines, and enterprise search that ship to production, not just a demo.
KEY RESPONSIBILITIES
- Build GenAI applications — LLM-powered features, copilot/chat experiences, enterprise search
- Design and implement RAG pipelines: chunking strategy, embedding selection, hybrid retrieval, re-ranking, GraphRAG where structured retrieval is needed
- Fine-tune and adapt models (LoRA/QLoRA) when prompt engineering and RAG aren't sufficient
- Engineer and version production prompts; build prompt/context management into the application layer
- Integrate LLM APIs (OpenAI, Anthropic, Azure OpenAI) and open-source model endpoints with auth, rate-limiting, and cost controls
- Instrument applications for evaluation — output logging, quality scoring, human-feedback loops
- Optimize latency and token cost through caching, batching, and model routing strategies
- Translate client business requirements into concrete GenAI feature specifications
- Communicate technical tradeoffs (cost, latency, accuracy) to non-technical product stakeholders
- Collaborate with the Agentic AI Architect and Data Scientists on shared components
- Document architecture and prompt design decisions for handoff and maintainability
REQUIREMENTS & SKILLS
- 4–8 yrs software engineering, with 1–3 yrs hands-on GenAI/LLM application building
- Strong Python; experience with LangChain, LlamaIndex, or equivalent orchestration frameworks
- Vector databases and embedding strategies (Pinecone, Weaviate, pgvector), plus knowledge-graph/graph-database tooling (Neo4j) where relevant
- Understands LLM failure modes (hallucination, context-window limits, cost blowup) and designs mitigations
- Experience with model fine-tuning techniques (LoRA/QLoRA) and evaluation harnesses
- Hands-on with enterprise GenAI/agentic platforms — Microsoft Azure AI Foundry, AWS Bedrock (incl. Strands Agents SDK), and Google Vertex AI; open-source frameworks (LangChain, LlamaIndex) a good-to-have where no platform is mandated
- API design and integration experience, including auth, rate limiting, and streaming responses
- Familiarity with prompt-versioning and LLMOps tooling (LangSmith, Weights & Biases, or similar)
- Clear technical writing — documents a RAG architecture for a non-technical stakeholder
- Comfortable working directly with client engineers during embedded delivery
- Collaborative — works with architects, data scientists, and QA without needing everything pre-specified
- Adaptable under ambiguity — prompt-based systems require rapid iteration and tolerance for imperfect first attempts
