AI Engineer/ Data Scientist
# AI Engineer/ Data Scientist **Simpliigence** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Engineer]` `[Data-Scientist]` `[Machine-Learning-Engineer]` `[LLM-Engineer]` `[AI-ML-Specialist]` `[Data-And-AI-Engineer]` `[Data-+-AI-Engineer]` `[AI-Data-Engineer]` `[AI-ML-Data-Engineer]` `[Artificial-Intelligence-Engineer]` `[AI-ML-Engineer]` `[Data-Science-Engineer]` `[Data-Science]` --- ### About the Role 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
- AI-Engineer
- Data-Scientist
- Machine-Learning-Engineer
- LLM-Engineer
- AI-ML-Specialist
- Data-And-AI-Engineer
- Data-+-AI-Engineer
- AI-Data-Engineer
- AI-ML-Data-Engineer
- Artificial-Intelligence-Engineer
- AI-ML-Engineer
- Data-Science-Engineer
- Data-Science
Наблюдалась 2026-10-19, впервые 2026-10-19, источник — Himalayas (JSON API).