Senior Machine Learning Engineer (LLM)

Overview

We’re looking for a hands-on Machine Learning Engineer with strong experience building and deploying real-world AI systems. This role focuses on designing scalable ML and LLM-powered solutions for processing large volumes of unstructured data and turning them into structured, actionable insights.

You’ll work at the intersection of machine learning, LLMs and data pipelines – owning systems end-to-end, from experimentation to production.

Responsibilities

• Design and build scalable ML/LLM pipelines for processing large-scale document and text data
• Develop and optimize LLM-based extraction workflows (classification, entity extraction, summarization, linking)
• Integrate LLMs into production systems, including prompt design, evaluation, and orchestration
• Work with structured and unstructured data, ensuring data quality and consistency across pipelines
• Implement entity resolution and data linking logic across multiple data sources
• Collaborate on building knowledge-oriented systems (e.g. search, semantic layers, or lightweight graph structures)
• Optimize models and pipelines for performance, cost, and reliability in production environments
• Contribute to system design, including scalability, fault tolerance, and maintainability
• Work closely with cross-functional teams to deliver and iterate on AI-driven features

Requirements

• 5+ years of experience in Machine Learning, Applied AI, or Data Engineering
• Strong Python skills and experience with ML/LLM ecosystems
• Hands-on experience deploying ML or LLM systems to production
• Experience with data processing frameworks (e.g. Spark, Beam, or similar)
• Solid understanding of ML fundamentals and NLP concepts
• Experience working with large datasets and building data pipelines
• Familiarity with LLM frameworks and tools (e.g. LangChain, LlamaIndex, or similar)
• Experience with SQL and data modeling
• Understanding of system design and scalable architectures
• Experience with Docker and cloud platforms (AWS, GCP, or Azure)

Nice to Have

• Experience with knowledge graphs or graph databases
• Experience building evaluation frameworks for LLM outputs
• Familiarity with vector databases and semantic search
• Experience working with on-prem or privacy-sensitive environments
• Exposure to backend development (e.g. APIs, microservices)

What We’re Looking For

• Strong ownership mindset and ability to work independently
• Pragmatic approach to building production-ready AI systems
• Ability to balance experimentation with delivery
• Comfortable working in fast-moving, ambiguous environments

Why This Role

• Real-world AI systems (not just PoCs)
• High ownership and impact
• Modern ML + LLM stack
• Opportunity to shape scalable AI solutions from the ground up