Senior Machine Learning Engineer

What you'll do

• Architect and evolve a highly scalable, multi-tenant AI/ML platform that unifies traditional ML (classification, regression, forecasting) and Generative AI/LLM orchestration.
• Design and implement production-grade AI Agents and Advanced Chatbots. Build reliable execution environments for Multi-Agent Systems, including state management, long-term memory architectures, and Model Context Protocol (MCP) server integrations.
• Build high-throughput, low-latency application backends and orchestration layers. Partner with data, platform, and full-stack engineers for seamless feature delivery and reliable production operations.
• Act as a technical anchor for the Data Science team – enforcing engineering standards, leading design and security reviews, evaluating build-vs-buy decisions, and mapping business requirements to technical designs.
• Evaluate trade-offs and drive adoption of modern AI infrastructure tools, optimized embedding pipelines, vector databases, and serverless compute paradigms (such as Workers AI).

Requirements

• Extensive experience as a Senior or Lead ML Engineer with a proven track record of architecting and operating production-grade ML platforms, services, and distributed backends.
• Strong competency in Traditional ML lifecycles (feature stores, training pipelines, model monitoring) alongside deep experience in Generative AI patterns (RAG pipelines, context engineering, fine-tuning, guardrailing, and agentic AI systems).
• Mastery of Python and robust experience with modern backend ecosystems. Familiarity with (or willingness to collaborate on) full-stack technologies like React and TypeScript is highly valued.
• A builder's mindset: comfortable navigating ambiguity, shaping your own technical roadmap, adapting as needed, and taking extreme ownership of system reliability, costs, and model performance.

Nice-to-haves

Technical Leadership & Systems Architecture

• 3+ years of dedicated ML Engineering experience within a large-scale, enterprise environment (handling petabyte-scale data and working across globally distributed teams).
• Proven ability to architect, scale, and secure reliable, highly observable distributed systems, with a track record of leveling up platform foundations.
• Experience mentoring engineers, leading by example through high-quality code and rigorous design reviews, and fostering a culture of technical excellence.
• Strong problem-solving skills with a demonstrated ability to independently drive complex projects through ambiguous spaces and collaborate cross-functionally with data engineers, full-stack teams, and analysts.

AI, LLMOps & Agentic Engineering

• Hands-on proficiency in building production-grade GenAI applications and multi-agent systems using advanced LLM frameworks like LangGraph, LangChain, or Autogen. Deep understanding of agent harness primitives, state management, memory architectures, and tool-calling loop mechanics.
• Experience establishing LLMOps foundations, including automated prompt tracking, LLM evaluation pipelines (e.g., Ragas, TruLens), vector database optimization, context/token management, and real-time guardrailing/moderation layers.
• Deep experience in scientific computing using Python (Scikit-Learn, PyTorch, or TensorFlow) and deploying traditional systems for end-to-end training, batch/real-time inference, and model observability.

Infrastructure, Cloud & Data Platforms

• Strong experience with Docker and Kubernetes for containerization and orchestration, alongside Infrastructure-as-Code tools like Terraform and public cloud ecosystems (GCP, AWS, or Azure).
• Hands-on experience with modern MLOps platform tools (e.g., Airflow, Argo Workflows, ArgoCD) and data systems including BigQuery, Postgres, and robust ETL/ELT practices.
• Experience with full-stack web technologies and serverless/edge environments (FastAPI, TypeScript/JavaScript, Cloudflare Workers), with the agility to contribute across a multi-language stack.
• Strong foundation in continuous integration/continuous deployment (CI/CD), testing frameworks (Pytest), and robust version control practices.

Education & Communication

• M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Exceptional written and verbal communication skills, with the ability to translate complex technical architectures into clear concepts for both engineering peers and business stakeholders.