AI Engineer/ Data Scientist
这是一个远程职位。我们正在寻找一个非常精明的 AI 工程师或高级数据科学家，帮助我们构建下一代智能，基于数据的产品。你将设计和运输生产级 LLM 应用程序，设计实际上在规模上工作的 RAG 管道，并为从快速设计到系统评估的一切带来严格的 ML 思维。这是一个具有高度自主性，高影响力的角色。你将与产品和工程密切合作以解决端到端的问题。· 设计，建造和部署 AI 模型，工具和代理商来自动化投资研究，组合管理，操作和报告中大量智能的工作流程。· 与数据与人工智能负责人合作，以确定 AI 使用案例的技术架构，以安全和管理的方式集成到 Snowflake,Databricks,内部 API 和事件流。·通过标准化的 MCP 服务器和客户端来安全地发现和连接到内部系统，数据库和服务，实现检索增强生成 (RAG) 管道和快速调节模式，将 LLMs 建立在马拉松专有数据，文档和知识资产基础上。·设计并实施基于模型语境协议 (MCP) 的集成，以便人工智能助理和代理能够安全地发现并连接到内系统，数据中心和服务。· 建立和维护 MCP 服务器，这些服务器包装了关键的企业服务 （数据仓库，文件存储，工作流程系统）并以标准的方式向 AI 客户端展示工具，资源和提示。· 为确保可靠的，可审计的 AI 互动与企业系统建立 MCP 主机/客户配置，访问控制和可观察模式。· 对模型和基于 MCP 的集成生命周期实施 MLOps 和 LLMOps 实践，包括部署自动化，监控，记录和反转战略。· 与数据工程师和平台团队合作，确保人工智能消费的清洁，安全和结构良好的数据访问，包括通过 MCP 暴露的系统治理。要求核心技能我们需要 Python （专家）LLMs & GenAI RAG 系统 快速工程 AWS ML / MLOps 矢量 DBs 模型版本 REST API Docker / containers 需求 · 6年以上实践 Python 你写生产代码，构建包装，并关心性能和可读性。·经验证明建立和运输 LLM 驱动应用程序 （不仅是包裹你理解罩下发生什么）. ·深入了解 RAG:密集检索。
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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).
Originally posted on Himalayas