Machine Learning Engineering Lead

Hackajob Ltd; Farringdon; On-site; GBP 85,000 - 115,000; Full time

Job summary

Hackajob Ltd is seeking a Machine Learning Engineering Lead to design, build and operate production ML/LLM-based services, with a focus on reliability and governance. You will own end-to-end ML workflows, including retrieval-augmented generation, embeddings, and semantic search, across legal content use cases.

You will mentor engineers, collaborate with cross-functional teams, drive MLOps, CI/CD, testing, monitoring, and incident response in AWS-hosted environments, while promoting responsible

Qualifications

• Hands-on experience in ML, software or data engineering.
• Experience designing and operating ML/LLM systems in production.
• Experience integrating AI/ML with enterprise systems and data platforms.
• Strong Python development skills for ML and data processing.
• Sound understanding of MLOps, CI/CD, testing, monitoring and observability.

Responsibilities

• Escalation point for AI/ML engineering issues within the area.
• Collaborate with engineers, product stakeholders and platform teams.
• Write and review specifications for complex ML/LLM components.
• Design, build, deploy, and operate production AI/ML services for legal use cases.
• Implement RAG, semantic search, embeddings, and citation-aware capabilities.
• Design agentic workflows and multi-step AI processes with governance.
• Integrate AI/ML with enterprise systems and AWS-hosted services.
• Establish evaluation and quality controls for accuracy and value.

Skills

ML engineering

Software engineering

Data engineering

Python for ML

Cross-functional collaboration

AWS/cloud environments

System design

MLOps

CI/CD

LLM/AI capabilities

Agentic workflows

Observability

Problem solving

Communication

Copilot/Codex

Docker

Kubernetes

Terraform

C#/.NET

SQL Server

Event-driven architecture

LegalTech knowledge

Tools

Docker

Kubernetes

Terraform

GitHub Copilot

Job description

Salary: £100,000 - 100,000 per year

Requirements:

• 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.
• 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.

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.
• Perform all other duties as assigned.

Technologies:

• AI
• AWS
• C#
• ChatGPT
• CI/CD
• Cloud
• Copilot
• Docker
• GitHub
• Support
• Kubernetes
• LLM
• Machine Learning
• MLOps
• Python
• RAG
• SQL
• Security
• Terraform
• Web
• XML
• ASP.NET

More:

We are partnering directly with LexisNexis to hire for this Machine Learning Engineering Lead role. Our software engineering team develops and supports business‑critical platforms used to create, manage, publish, and analyse legal and regulatory content. Our work spans modern web applications, cloud services, content migration programmes, publishing platforms, reporting solutions, and operational tooling, and we partner with editorial, product, and technology stakeholders to deliver high‑quality solutions that drive business value. We support the UK business and collaborate closely with engineering teams across multiple regions to share expertise, promote reuse, and deliver scalable solutions. At LexisNexis GTO, you will work on business‑critical products while using the latest AI and software engineering technologies. We invest in developer productivity tools such as GitHub Copilot, ChatGPT, and Codex, and we offer training, workshops, office hours, and communities of practice. You will also benefit from flexible working, a strong culture of learning and collaboration, and a range of benefits including generous holiday allowance, health screening, private medical benefits, wellbeing programmes, life assurance, pension, leave options, employee assistance, learning and development resources, discounts, and more. We are committed to a fair and accessible hiring process and an inclusive workplace.

last updated 38 week of 2026