Forward Deployed Engineer - GenAI

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