AI Engineer / Data Scientist

New

MarathonAI and data

Source API remote eligibility restrictions: United StatesFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 6+ years of hands-on Python
Required Skills: AWSDockerPythonMachine LearningRESTful APIsPrompt EngineeringMLOps

Have 6+ years of hands-on Python experience, including writing production code and building packages.
Bring proven experience building and shipping LLM-powered applications.
Understand RAG, including dense and sparse retrieval, hybrid retrieval, re-ranking, context window management, and evaluation.
Have strong prompt engineering skills, including structured outputs and systematic hallucination debugging.
Have hands-on AWS experience with SageMaker, S3, Lambda, and CloudWatch.
Have solid ML fundamentals, including model training, evaluation, bias and variance, feature engineering, and experimental design.
Be comfortable with embedding models, tokenization internals, KV-cache, quantization trade-offs, and latency optimization.
Use Python, LLMs and GenAI, RAG systems, AWS, ML/MLOps, vector databases, model versioning, REST APIs, and Docker or containers.

Design, build, and deploy AI models, tools, and agents for investment research, portfolio management, operations, and client reporting workflows.
Partner with the Head of Data & AI to define technical architecture for AI use cases.
Integrate AI use cases with Snowflake, Databricks, internal APIs, and event streams securely and with governance.
Implement RAG pipelines and prompt conditioning patterns grounded in proprietary data, documents, and knowledge assets.
Design MCP-based integrations that connect AI assistants and agents to internal systems, databases, and services.
Build MCP servers that expose enterprise service tools, resources, and prompts to AI clients.
Establish MCP host and client configuration, access control, and observability patterns.
Implement deployment automation, monitoring, logging, and rollback strategies for model and MCP integration lifecycles.
Collaborate with data engineers and platform teams on secure, well-structured data access and governance of systems exposed via MCP.

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