Forward Deployment Engineer

Title : Forward Deployment Engineer

Location: Remote

Must-Have Technical Skills

• Agentic and GenAI - LangChain, LangGraph, LlamaIndex, Semantic Kernel, Model Context Protocol (MCP), multi-agent patterns with production usage, not tutorials
• Evaluation and safety - LLM evaluation frameworks, guardrails, red-teaming, prompt-injection defences
• Full-stack AI application development - End-to-end working system, with a UI, a backend and an LLM integration, in weeks rather than months
• Python + FastAPI/Flask for backend services, Streamlit / Databricks Apps / lightweight React for user-facing interfaces
• Direct hands-on with at least one of: Snowflake Cortex (Analyst/Search/Agents/LLM Functions) OR Databricks Genie (Genie Spaces, semantic models) - you know these products well enough to configure, tune, and integrate them into vertical solutions
• RAG pipeline construction - chunking, embeddings, vector search (Pinecone, pgvector, Chroma, FAISS, Azure AI Search, Snowflake Cortex Search), retrieval evaluation, grounding, citation
• Prompt engineering with evaluation discipline - you know how to design prompts, evaluate them against ground truth, iterate based on hallucination and accuracy metrics
• Cloud data platform fluency - Snowflake and/or Databricks at working depth, plus at least one cloud provider (Azure preferred given the client environment, AWS/Google Cloud Platform acceptable) and it's AIML services.
• SQL and data modelling - sufficient to work directly with governed datasets and semantic models
• Git, CI/CD, and modern development workflows - you own the deployment path, not just the local development
• API design and integration - you can integrate your solution into existing enterprise systems (SAP, Workday, Coupa, ERP, etc.) via REST APIs

• Basic MLOps awareness - you understand model versioning, prompt versioning, evaluation harnesses, observability (LangSmith, Datadog, Application Insights) - enough to hand off your solution to the MLOps team cleanly

Nice to Have

• Pharma, life sciences, or CPG Finance domain experience - familiarity with FP&A processes, financial consolidation, regulatory reporting, cost allocation, or clinical trial finance
• Veeva CRM, IQVIA, SAP S/4HANA, SAP BW, Oracle Financials, Workday Adaptive or similar enterprise Finance tooling
• Regulated environment delivery - SOX, GxP, data residency, audit trails
• Snowflake Cortex certification, Databricks certification, or Azure AI Engineer Associate (AI-102)
• Prior FDE, Solutions Engineer, Sales Engineer, or Field Engineer experience at Palantir, Snowflake, Databricks, OpenAI, Anthropic, or similar
• Startup / small-team experience - you've had to wear multiple hats and ship end-to-end without organizational scaffolding
• Direct experience with agentic workflows (multi-agent orchestration, human-in-the-loop, tool-calling) in production, not just demos