Jobs → Engineering → Forward Deployment Engineer

About the Role An FDE sits at the intersection of AI platforms and business users, translating ambiguous challenges into working AI solutions, deploying them into customer environments, and driving measurable value through rapid iteration. You are part engineer, part consultant, and part translator between business stakeholders and technical delivery. Key Responsibilities • End-to-End Delivery: Own solutions from initial stakeholder conversation through deployment, adoption, and outcome measurement. • Rapid Prototyping: Build functional UI apps (Streamlit, Databricks Apps, lightweight React) within 1–2 weeks to gather user feedback. • Vertical AI Applications: Leverage Snowflake Cortex, Databricks Genie, RAG pipelines, and agentic workflows to solve high-value business problems. • Governance & Compliance: Build in data privacy, GxP-aware controls, and RBAC from day one. • The "Last Mile": Own user training, adoption support, edge cases, and change management. Requirements • Agentic & GenAI: Production experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, and multi-agent patterns. • Full-Stack & APIs: Python (FastAPI/Flask) for backends; Streamlit or lightweight React for frontends; REST API integration. • Data Platforms: Hands-on expertise with Snowflake Cortex or Databricks Genie, plus cloud platforms (Azure preferred). • RAG & Prompt Engineering: Chunking, embeddings, vector search (Pinecone, pgvector, Azure AI Search), and evaluation discipline against ground truth. • Foundations: Strong SQL, data modeling, Git, CI/CD, and basic MLOps awareness (LangSmith, Datadog). Must-Have Non-Technical Skills • Direct Stakeholder Communication: Ability to translate non-technical business pain points into technical roadmaps. • Ambiguity Tolerance: Comfort starting with vague problem statements and refining via rapid prototyping. • Product Mindset: Optimized for user adoption and business impact over elegant, over-engineered architectures.