Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.
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 skills we need
Python (expert)
LLMs & GenAI
RAG systems
Prompt engineering
AWS
ML / MLOps
Vector DBs
Model versioning
REST APIs
Docker / containers
Requirements
- 6+ years of hands-on Python — you write production code, build packages, and care about performance and readability.
- Proven experience building and shipping LLM-powered applications (not just wrappers — you understand what's happening under the hood).
- Deep understanding of RAG: dense retrieval, sparse retrieval, hybrid, re-ranking, context window management, and evaluation.
- Strong prompt engineering skills: you know when to use CoT, how to design structured outputs, and how to debug hallucinations systematically.
- Hands-on AWS experience — SageMaker, S3, Lambda, CloudWatch, and ideally Bedrock or SageMaker JumpStart.
- Solid ML fundamentals: model training, evaluation, bias/variance, feature engineering, and experimental design.
- Comfortable with embedding models, tokenization internals, KV-cache, quantization trade-offs, and latency optimization.
Nice to have
- Experience with agentic frameworks (LangChain, LlamaIndex, CrewAI, or custom tool-use implementations).
- Fine-tuning experience — LoRA, QLoRA, instruction tuning, RLHF familiarity.
- Familiarity with evaluation frameworks: RAGAS, DeepEval, or building custom eval harnesses.
- MLOps tooling: MLflow, DVC, Weights & Biases, or SageMaker Pipelines.
- Knowledge of streaming inference, async serving, and cost optimization for token-heavy workloads.
- Prior work in analytics, BI, or domain-specific NLP (finance, healthcare, e-commerce).
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