Machine Learning Engineering Lead

Key Responsibilities

• Serve as the initial point of escalation for AI/ML engineering issues within the area of responsibility.
• Interface with software engineers, data engineers, product stakeholders, domain experts, platform teams, and other technical personnel to finalise requirements and clarify integration needs.
• Write and review portions of detailed specifications for the development of complex AI/ML, LLM, RAG, and agentic workflow components.
• Design, build, integrate, deploy, and operate production AI/ML and LLM-based services for legal research, analytics, and content use cases.
• Implement RAG, semantic search, embeddings-based retrieval, ranking, summarisation, classification, content enrichment, and citation-aware AI capabilities where appropriate.
• Design and implement agentic workflows, tool orchestration, and multi-step AI processes that are reliable, traceable, and governed.
• Integrate AI/ML capabilities with enterprise systems, APIs, databases, data platforms, content repositories, legacy applications, internal services, and AWS-hosted services.
• Establish evaluation and quality controls for accuracy, groundedness, citation quality, hallucination risk, agent task success, latency, cost, reliability, and business value.
• Successfully implement development processes, coding best practices, code reviews, MLOps practices, and responsible AI controls.
• Apply AI-assisted development tools to reduce software development cycle time and support code explanation, test generation, refactoring, debugging, documentation, code review, migration planning, and legacy system analysis.
• Resolve complex technical issues related to AI/ML services, data flows, system integration, model behaviour, production support, and operational reliability.
• Mentor and/or train engineers as directed by department management, ensuring they are knowledgeable in critical aspects of AI/ML engineering, MLOps, SDLC practices, and responsible use of AI-assisted development tools.
• Keep abreast of relevant technology developments in machine learning engineering, LLMs, agentic workflows, AWS cloud services, responsible AI, and software engineering practices.
• Ensure AI/ML solutions align with enterprise data governance, security, privacy, responsible AI, and operational standards.
• All other duties as assigned.

Qualifications -

• Significant hands-on experience in machine learning engineering, software engineering, data engineering, or a related technical discipline.
• Experience designing, building, deploying, and operating ML, AI, LLM, or data-driven systems in production.
• Experience integrating AI/ML services with enterprise systems, APIs, databases, data platforms, legacy applications, or internal services.
• Experience working with cross-functional teams to understand business processes, data flows, content repositories, integration points, and operational constraints.
• Experience working with AWS or cloud-hosted production environments.
• Equivalent technical experience or education considered.

Technical Skills-

• Strong Python development skills for machine learning engineering, data processing, automation, service development, and production AI/ML workflows.
• Strong software engineering background, including system design, APIs, distributed systems, automated testing, code review, maintainability, reliability, and production support.
• Strong understanding of ML engineering and MLOps practices, including model lifecycle management, CI/CD, testing, monitoring, release management, observability, and operational support.
• Practical experience with LLM-based capabilities, including retrieval-augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability.
• Experience with agentic workflows, tool orchestration, and multi-step AI processes.
• Strong AWS knowledge, including cloud-hosted applications, data services, security controls, logging, monitoring, and production support.
• Strong understanding of SDLC practices, including requirements analysis, design, implementation, automated testing, code review, secure coding, deployment, and production support.
• Strong understanding of responsible AI practices, including evaluation, traceability, secure data handling, model governance, human oversight, and risk management.
• Ability to work with structured, semi-structured, and unstructured data sources.
• Ability to understand legacy systems, domain processes, data flows, and integration constraints.
• Practical experience using AI-assisted development tools such as GitHub Copilot, Codex, Claude, or similar tools to improve software delivery.
• Strong problem-solving skills, including identifying, researching, troubleshooting, and resolving complex technical, data, and integration issues.
• Strong communication and technical writing skills, including the ability to explain ML and engineering concepts clearly to technical and non-technical stakeholders.
• Desirable experience with Docker, Kubernetes/K8s, AWS EKS or ECS, Terraform, or similar cloud deployment technologies.
• Desirable working knowledge of C#/.NET and SQL Server, particularly for integration with enterprise or legacy systems.
• Desirable experience with event-driven architecture, messaging, queues, asynchronous processing, retries, idempotency, and failure handling.
• Desirable experience with legal content systems, LegalTech, publishing platforms, case law, citation systems, legal research workflows, XML/XSLT, structured content processing, search, ranking, indexing pipelines, or content enrichment.

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