Engineering Manager, AI

Job Title: Engineering Manager, AI

Company: 167 Solutions

Location: Hybrid (UK ONLY)

Salary: £150,000 - £170,000 per annum

Contract Type: Permanent

Experience Level: Minimum 5 years' experience

Start Date: ASAP

167

Solutions is partnering with a globally recognised media and information services organisation to recruit an
Engineering Manager – Applied AI & Data Science
.

Our client is at the forefront of digital innovation, investing heavily in artificial intelligence, machine learning and data science to transform how millions of people access and interact with trusted information.

The role combines people leadership, technical architecture and hands-on engineering, with a particular focus on generative AI, agentic systems, LLMOps and AI-native software development.

You'll play a key role in shaping the organisation's AI capabilities, delivering production-grade AI systems and driving the adoption of modern AI engineering practices.

As Engineering Manager, you will lead the Applied AI and Data Science team, taking ownership of technical delivery, team development and engineering standards while remaining actively involved in designing and building AI solutions.

You will work closely with senior AI leadership, product managers, data engineers and business stakeholders to translate ambitious AI initiatives into scalable, secure and commercially valuable solutions.

The team's work spans large language models, agentic AI, retrieval-augmented generation (RAG), natural language processing, predictive modelling and shared AI platform capabilities.

• Lead and develop a multidisciplinary AI team
  , providing day-to-day management, mentorship, technical guidance and career development for AI/ML Engineers, NLP Engineers and Data Scientists.
• Own technical delivery
  , managing priorities, resources and engineering commitments while balancing rapid experimentation with production requirements.
• Remain hands-on
  , contributing Python code, reviewing models and implementations, prototyping solutions and supporting complex technical challenges.
• Shape AI architecture
  , guiding the design and development of agentic AI systems, RAG pipelines, NLP solutions and machine learning applications.
• Build safe and reliable AI agents
  , implementing appropriate guardrails, tool permissions, human-in-the-loop controls and end-to-end evaluation.
• Drive AI-native software engineering
  , introducing and scaling the use of tools such as Claude Code, Cursor and Git Hub Copilot across development, testing and code review.
• Establish engineering best practices
  , covering LLMOps, MLOps, CI/CD, automated evaluation, observability, security, performance and cost optimisation.
• Work extensively with AWS AI services
  , including Amazon Bedrock, Bedrock Agent Core, Sage Maker, Lambda and Step Functions.

• Proven experience as an Engineering Manager or Technical Lead with direct people-management responsibility for AI Engineers, ML Engineers, NLP Engineers or Data Scientists.
• Strong hands-on AI/ML engineering background, with advanced Python skills and experience developing production-grade AI systems.
• Experience designing and building LLM-powered and agentic AI applications, including RAG, NLP and traditional machine learning solutions.
• Practical experience with modern agent frameworks such as Lang Graph, Strands Agents or Claude Agent SDK, alongside interoperability standards including MCP and A2A.
• Strong AWS cloud engineering experience, particularly Amazon Bedrock, Bedrock Agent Core, Sage Maker, Lambda and Step Functions, with Infrastructure as Code using AWS CDK or equivalent.
• Hands-on experience with LLMOps and MLOps, including model and agent evaluation, trajectory testing, tracing, observability, guardrails and cost monitoring.
• Understanding of AI security challenges, including prompt injection, data leakage, excessive agent permissions and appropriate mitigation strategies.
• Experience using AI coding assistants and agents, such as Claude Code, Cursor or Git Hub Copilot, throughout the software development lifecycle.
• Ability to establish engineering standards for AI-generated code, context engineering, automated testing and AI-assisted development.

• Experience building and operating production-grade agentic AI systems, including multi-agent orchestration, agent memory, MCP-based tool integration and agentic RAG.
• Strong NLP expertise, including semantic search, summarisation, information extraction, classification or LLM…