Simpliigence
Skills: Python, AWS, Docker
This is a remote position.
We're looking for an exceptionally sharp AI Engineer or Senior Data Scientist to join and help us build the next generation of intelligent, data-driven products. You'll architect and ship production-grade LLM applications, design RAG pipelines that actually work at scale, and bring rigorous ML thinking to everything from prompt design to system evaluation. This is a high-autonomy, high-impact role — you'll work closely with product and engineering to own problems end-to-end.
Design, build, and deploy AI models, tools, and agents to automate intelligence-heavy workflows in investment research, portfolio management, operations, and client reporting. Partner with the Head of Data & AI to define the technical architecture for AI use cases, integrating with Snowflake, Databricks, internal APIs, and event streams in a secure and governed way. Implement retrieval augmented generation (RAG) pipelines and prompt conditioning patterns that ground LLMs on Marathon’s proprietary data, documents, and knowledge assets. Design and implement Model Context Protocol (MCP)–based integrations so AI assistants and agents can securely discover and connect to internal systems, databases, and services through standardized MCP servers and clients. Build and maintain MCP servers that wrap key enterprise services (data warehouses, document stores, workflow systems) and expose tools, resources, and prompts to AI clients in a standardized way. Establish patterns for MCP host/client configuration, access control, and observability to ensure reliable, auditable AI interactions with enterprise systems. Implement MLOps and LLMOps practices for both model and MCP-based integration lifecycles, including deployment automation, monitoring, logging, and rollback strategies. Collaborate with data engineers and platform teams to ensure clean, secure, and well-structured data access for AI consumption, including governance of which systems are exposed via MCP.
Requirements
Core skill…
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