Forward Deployment Engineer
IT America
United States
1 day ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
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)
+43 more
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
Job 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 itβs 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
Requirements
- 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 - youβve 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
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