Position Title: Forward Deployed Engineer (AI & Finance Data)
Location: Minneapolis, MN & Hartford, CT (Onsite)
Duration: 6 Months Contract
Job Description:
Forward Deployed Engineer (FDE) AI & Finance Data
We are looking for Forward Deployed Engineers (FDEs) and technical specialists to form a dedicated POD supporting a large-scale Finance and Accounting data transformation initiative with a strong focus on AI enablement.
The team will work closely with business and technology stakeholders to design, build, integrate, and deploy scalable data and AI solutions across data engineering, architecture, integration, semantic modeling, and knowledge management.
Candidates should be highly hands-on, comfortable working across functions, and able to translate business requirements into production-ready solutions.
The objective is to establish a trusted, scalable data foundation that enables advanced analytics, automation, GenAI, and emerging Agentic AI use cases.
Role Overview:
We are seeking an enterprise focused Forward Deployed Engineer (FDE) specializing in Agentic AI, Snowflake and AWS to accelerate enterprise workflow automation and business transformation.
In this role, you will sit at the intersection of modern cloud architecture, enterprise data platforms, and production AI.
You will embed directly with customer engineering and line-of-business teams to architect, build, and deploy autonomous multi-agent systems, natural language data interfaces, and tool-calling workflows grounded directly in enterprise data stores.
Your primary mission is to leverage the joint power of Snowflake (Cortex AI, Analyst/Search services, Horizon Catalog) and AWS (Amazon Bedrock, Serverless compute, MCP integration) to dramatically reduce workflow friction, automate high-value decisions, and rapidly move enterprise AI initiatives from PoC to governed, production-scale execution.
Key Responsibilities:
Agentic AI & Enterprise Data Architecture
Build & Deploy Autonomous AI Workflows: Design, test, and productionize multi-step AI agents that leverage Snowflake Cortex AI (including CoCo) and additionally AWS Kiro/Bedrock where needed.
Data Integration: Build secure data access layers connecting AI agents to Snowflake data warehouses/lakes, AWS services using the Model Context Protocol (MCP), REST/gRPC APIs, and native connectors and external tools like Jira/ServiceNow.
Unified Data & Document Retrieval: Build hybrid search and RAG pipelines combining structured queries (via Snowflake Cortex Analyst / SQL generation) and unstructured document search (via Snowflake Cortex Search and/or Bedrock Knowledge Bases).
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