Machine Learning Engineering Lead

About the role

We are looking for a Machine Learning Engineering Lead to design, build and operate intelligent AI/ML services that power our legal‑content products. You will act as the technical authority for production ML, LLM and agentic workflow capabilities, guiding architecture, MLOps and responsible AI practices. The role does not have direct reports but involves mentoring engineers and collaborating across multiple regions.

Key responsibilities

• Serve as the primary escalation point for AI/ML engineering issues and work with software, data and platform teams to define requirements.
• Design, integrate, deploy and operate production AI/ML, LLM and retrieval‑augmented generation services for legal research and analytics.
• Implement agentic workflows, tool orchestration and multi‑step AI processes that are reliable, traceable and governed.
• Integrate AI/ML capabilities with enterprise APIs, databases, content repositories, legacy applications and AWS‑hosted services.
• Establish evaluation and quality controls for accuracy, hallucination risk, latency, cost and business value.
• Apply MLOps best practices, code reviews and responsible AI controls while using AI‑assisted development tools.

Required profile

• Significant hands‑on experience in machine learning engineering, software engineering or data engineering.
• Proven track record designing, building, deploying and operating ML/AI/LLM systems in production.
• Experience integrating AI/ML services with enterprise systems, APIs, databases and legacy applications.
• Experience working with AWS or other cloud‑hosted production environments.

Required skills

• Strong Python development for ML engineering, data processing and service development.
• Deep knowledge of AWS services, cloud‑hosted applications, security and monitoring.
• Practical experience with LLM‑based capabilities such as retrieval‑augmented generation, semantic search, embeddings and prompt design.
• Experience with MLOps, CI/CD pipelines, model lifecycle management and observability.
• Familiarity with Docker, Kubernetes (EKS/ECS) and Terraform for cloud deployment.
• Working knowledge of C#/.NET and SQL Server for legacy system integration.
• Use of AI‑assisted development tools (e.g., GitHub Copilot, Claude) to improve software delivery.

What we offer

• Flexible working hours and hybrid work options to support work‑life balance.
• Generous holiday allowance with the option to purchase additional days.
• Health screening, eye‑care vouchers and private medical benefits.
• Wellbeing programs, shared parental leave, study assistance and sabbatical opportunities.