We are seeking a Machine Learning Engineer Lead to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development.
In this role you will be a hands-on engineer and leader that will lead a high-performing team of 4-5 ML engineers, drive platform-level decisions, and ensure enterprise-grade scalability, reliability, and responsible AI compliance.
Responsibilities:
- Lead, mentor, and grow a team of 4-5 ML engineers.
- Provide architectural direction and code-level guidance.
- Establish engineering best practices for ML system design, testing, and deployment.
- Conduct design reviews, performance reviews, and technical roadmap planning.
- Architect distributed ML systems serving multiple global products.
- Standardize infrastructure patterns for LLM serving and retrieval systems.
- Define and implement enterprise-ready agentic frameworks.
- Architect multi-step reasoning systems.
- Lead decisions on deterministic workflows vs. autonomous agents.
- Implement guardrails, safety layers, and traceability mechanisms.
- Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
- Establish CI/CD standards for ML lifecycle management.
- Ensure compliance with enterprise data governance and responsible AI standards.
Requirements
- 8-10 years of Machine Learning/Software Engineer experience
- 2-3 years of people management experience.
- Master’s degree or bachelor's degree, computer science degree is highly desirable.
- Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data
- Experience with ML deployment to production