Machine Learning Engineer Lead

About the role

WhiteCrow is a global talent research, insight and pipelining specialist with offices in the UK, USA, Singapore, Malaysia, Hong Kong, Dubai, and India. Our international reach helps us understand and penetrate specialist markets at a global level, and we complement our clients’ in‑house research capabilities. Our client is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision‑making and outcomes, and advance the rule of law worldwide. They support lawyers, corporations, universities, governments, courts, and global associations in areas ranging from case‑winning and brand monitoring to evidence collection against war criminals and tools to combat human trafficking.

Responsibilities

• Leading, mentoring, and growing a team of 4–5 ML engineers.
• Providing architectural direction and code‑level guidance.
• Establishing engineering best practices for ML system design, testing, and deployment.
• Conducting design reviews, performance reviews, and technical roadmap planning.
• Architecting distributed ML systems serving multiple global products.
• Standardizing infrastructure patterns for LLM serving and retrieval systems.
• Defining and implementing enterprise‑ready agentic frameworks.
• Architecting multi‑step reasoning systems.
• Leading decisions on deterministic workflows versus autonomous agents.
• Implementing guardrails, safety layers, and traceability mechanisms.
• Developing evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
• Establishing CI/CD standards for ML lifecycle management.
• Ensuring 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.

Skills

• Strong software engineering background with experience in building system design, architecting AI feature/products that cater to large numbers of users and deal with large volumes of unstructured data.