Forward Deployment Engineer

Quick Facts

• Role: Forward Deployment Engineer

• Location: Remote

Description

Build and deploy end-to-end AI applications in production, including UI, backend, and LLM integration. You’ll construct RAG and agentic workflows with an evaluation-and-safety mindset, then integrate solutions into enterprise systems. Work across cloud data platforms (Snowflake/Databricks) and modern APIs, with enough MLOps awareness to hand off cleanly to production teams.

Responsibilities

• Deliver production-ready agentic and GenAI systems (multi-agent workflows, tool-calling, human-in-the-loop)

• Build RAG pipelines with retrieval evaluation, grounding, and citations

• Design prompts and iterate using evaluation metrics for accuracy and hallucinations

• Develop and deploy full-stack AI applications (UI + backend + LLM integration)

• Integrate solutions into enterprise environments via REST APIs

• Apply LLM safety practices (guardrails, red-teaming, prompt-injection defenses)

Requirements

• Production experience using agentic/GenAI frameworks such as LangChain/LangGraph/LlamaIndex/Semantic Kernel and multi-agent patterns

• Knowledge of LLM evaluation and safety techniques: LLM evaluation frameworks, guardrails, red-teaming, prompt-injection defenses

• Experience shipping full-stack AI systems with UI, backend, and LLM integration in weeks

• Python backend development experience with FastAPI or Flask

• Experience configuring, tuning, and integrating either Snowflake Cortex or Databricks Genie into vertical solutions

• RAG pipeline construction skills (chunking, embeddings, vector search, retrieval evaluation, grounding, citation)

• Prompt engineering with an evaluation discipline and iteration based on ground truth metrics

• Working knowledge of Snowflake and/or Databricks plus a cloud provider (Azure preferred)

• Strong SQL and data modeling skills for governed datasets and semantic models

• Deployment experience with Git and CI/CD; ownership of the deployment path

• API integration skills for enterprise systems via REST APIs

• Basic MLOps awareness: model/prompt versioning, evaluation harnesses, observability for handoff to MLOps

Benefits

• Opportunity to ship production AI systems quickly with hands-on engineering across the full stack and deployment pipeline.