Machine Learning Engineer, Platform
Builds and deploys retrieval, knowledge representation, and ML platform components for enterprise generative AI systems. The role requires 5+ years of production ML/AI experience, strong Python skills, and expertise in RAG, embeddings, vector indexing, and semantic search.
About the job
Responsibilities
- Own large areas of the platform end to end, driving components from design through production deployment.
- Build knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data.
- Design and implement retrieval-augmented generation (RAG) pipelines, including chunking, embedding, indexing, retrieval, and reranking.
- Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services.
- Develop context retrieval systems that balance recall, precision, latency, and cost.
- Build evaluation frameworks, datasets, and metrics to measure retrieval quality, context relevance, and end-to-end agent performance.
- Build reliable backend services and data pipelines supporting ML and large language model components in production.
- Deliver experiments and new capabilities quickly while maintaining high quality and tight customer feedback loops.
- Collaborate across product, ML, and infrastructure teams to shape the platform's direction.
Requirements
- 5+ years of experience building and deploying machine learning or AI systems for real-world production use cases.
- Master's or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
- Deep, hands-on understanding of retrieval systems, RAG, embeddings, vector indexing, and knowledge representation.
- Experience with knowledge representation, semantic search, or agentic systems.
- Proficiency in Python, including production-quality, testable, and maintainable code.
- Experience scaling or shipping products at high-growth startups.
- Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
- Strong communication skills and comfort working in customer-facing or cross-functional environments.
Skills
Python, Machine Learning, Artificial Intelligence, RAG, Embeddings, Vector Indexing, Knowledge Graphs, Ontologies, Semantic Search, Vector Databases, Backend Services, Data Pipelines, Evaluation Frameworks, LLMs, Agentic Systems
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