What are the responsibilities and job description for the Lead ML Ops Engineer position at Beacon Talent?
Looking to lead the machine learning function at a company applying a clear B2B application of Agentic AI to the supply chain indusrtry? Let's talk! My client is Seed stage (YC'24) applying Agentic AI to the supply chain sector. They have paying customers, have 3x'ed ARR in the last 6-months and proven traction.
This Staff/Lead Machine Learning Engineer will build the end-to-end ML platform. We need an experienced engineer with a depth of experience in ML Ops. You understand what it means to be in an early stage environment. You are going to wear a lot of hats and have to rollup your sleeves with a small team of hitters. This is hybrid, we're in office 3-4 days per week. The environment is fun, high-energy and everyone has a get it done attitude.
Now for the particulars:
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Train and fine-tune LLM foundation models (e.g., GPT, Claude, PaLM 2, LLaMA) using cutting-edge techniques and frameworks, ideally on AWS SageMaker.
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Design, implement, and optimize embedding models for a variety of applications.
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Build and deploy AI-powered chatbots using frameworks like LangChain or LangGraph.
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Integrate and manage vector databases (e.g., MongoDB Atlas Vector Store, Milvus, Weaviate, Pinecone) to support efficient model querying and retrieval.
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Collaborate closely with cross-functional teams to align AI-driven solutions with business objectives in the supply chain domain.
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Write clean, maintainable, and scalable code in Python; TypeScript experience is a strong plus.
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Drive the end-to-end lifecycle of machine learning models, from research and experimentation to production deployment and monitoring.
The bare minimums:
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Experience: 3 years of hands-on experience as a Machine Learning Engineer with a focus on developing and deploying production-ready solutions.
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Foundational Models: Experience training and fine-tuning LLMs (GPT, Claude, Gemini/PaLM 2, LLaMA, etc.).
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Embedding Models: Strong expertise in designing and implementing embedding-based solutions.
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Vector Databases: Practical knowledge of vector databases (MongoDB Atlas Vector Store, Milvus, Weaviate, Pinecone, etc.).
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AI Agents: Hands-on experience building AI-powered chatbots, ideally using LangChain or LangGraph.
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Technical Skills: Advanced proficiency in Python.
