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).
Similar Jobs
Software
Own the complete data science lifecycle for a flagship predictive modeling project. Responsibilities include analyzing and engineering multi-source data, building production-grade pipelines, developing and deploying supervised, unsupervised, reinforcement, deep learning, NLP, and forecasting models, monitoring performance, documenting work, communicating insights, and mentoring junior team members.
Top Skills:
AirflowSparkArimaAWSAzure MlBigQueryCatboostCi/CdDbtDockerFastapiFlaskGCPGenerative AiGitGitGlueHugging Face TransformersKubeflowKubernetesLightgbmLimeLlmsLstmMatplotlibMlflowNumpyPandasPlotlyPrefectPrompt EngineeringProphetPysparkPythonPyTorchS3SagemakerScikit-LearnShapSnowflakeSnowflake Cortex AiSnowparkSQLStatsmodelsTensorFlowVertex AiXgboost
Agency • Information Technology
Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.
Top Skills:
Ai Observability PlatformsAws BedrockAzure Ai FoundryClaudeDatabricksEmbeddingsGeminiKubernetesMlflowNeo4JNumpyOpen-Source LlmsOpenaiPandasPythonPyTorchRagScikit-LearnSemantic SearchSparkTensorFlowVector Databases
31 Minutes Ago
Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Lead global deployment of IL6S and operational excellence across manufacturing networks: develop frameworks, run maturity assessments, standardize processes, drive digital tool adoption, mentor site teams, and track KPIs to improve OEE, yield, waste, and productivity.
Top Skills:
Advanced AnalyticsIl6SIotLeanSix SigmaTpm
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
