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Mid-level AI role, metro location, popular title and broad skillset create high candidate competition.
Strong ML/LLM engineering skills transfer across industries, but regulatory domain knowledge increases fit sensitivity.
Mandatory 3+ years and many specific LLM, infra, and database requirements increase shortlisting strictness.
Develop and deploy AI-powered applications leveraging large language models (LLMs) and generative AI frameworks to extract insights from complex regulatory and inspection datasets.
Design and maintain scalable backend APIs and microservices using Python and FastAPI to integrate AI capabilities into the existing platform.
Ensure AI systems operate reliably in production through testing, monitoring, performance optimization, and troubleshooting with cross-functional teams.
Minimum 3 years experience as ML Engineer with productionizing traditional ML and/or Generative AI applications.
Proficient in Python and experienced with LLM provider APIs (OpenAI, Google, Anthropic, Amazon Bedrock) and open-source LLMs (Llama, Mistral).
Hands-on experience with building conversational AI using agentic frameworks (LangChain, LlamaIndex, LangGraph, CrewAI) and microservices architecture with FastAPI.
Bachelor's degree in Computer Science, Computer Engineering, or related technical field.
Experienced with integrating AI systems into complex distributed services and data pipelines involving SQL and NoSQL databases, vector and graph databases, and hybrid search.
Familiar with scalable AI inference deployment and production environment management, including performance tuning and troubleshooting.
Operates well in cross-functional teams translating product requirements into AI-driven solutions within fast-evolving regulatory data contexts.