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# Forward Deployment Engineer

- **Company:** IT America
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Application Performance Management, ARM Architecture, Audit Trail, Microsoft Azure, Continuous Integration, Python (Programming Language), OpenAI, Salesforce.Com, SAP (Applications), SAP NetWeaver Business Warehouse, Search Technologies, SQL Databases, Management of Software Versions, Enterprise Search, Datadog, Pinecone, Google Cloud, Oracle Hyperion, Cloud Platform System, LangChain, ReactJS, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Llamaindex, Backend, Agentic-AI, Git, Fastapi, Pgvector, LangSmith, SAP S/4HANA, Coupa Procurement, Machine Learning Operations, FAISS, Azure AI, Evaluation of Large Language Models, Api Design, Restful APIs, Model Context Protocol, Streamlit Framework, Semantic Kernel, Artificial Intelligence Markup Language (AIML), Software Version Control, Human in the Loop, Workday, GXP, ChromaDB, Databricks
- **Published:** November 14, 2026
- **Apply:** https://www.dice.com/job-detail/2b88fc1f-63e5-408a-83fb-ffe9dffd7823

## About the Role

* 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

## Description

* 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