Lead AI Engineer: WeBuild-AI in London

Role: Lead AI Engineer

Lead AI Engineer role at WeBuild-AI is based in London, England, with a hybrid arrangement that includes office attendance three days each week. The role leads the design and development of AI solutions for enterprise clients. Its engineering work includes customising Autogen and LangGraph, connecting AI systems with client platforms and data sources, and turning requirements into secure, scalable applications.

WeBuild-AI works with language models, agent frameworks, and its Pathway platform. The Lead AI Engineer oversees technical quality, supports client AI adoption, and mentors mid-level and junior engineers. The position is built around production-grade AI and machine learning systems, with collaboration across data engineering, client, and cross-functional teams.

WeBuild-AI is an AI careers employer focused on enterprise AI strategy, data, digital transformation, and deployment. Its services include AI governance and risk management, AI-ready data and infrastructure, custom AI solutions, AI operating model design, and AI agents and automation. The company operates as an enterprise AI consultancy for global organisations. The role therefore connects engineering decisions with client systems, business requirements, delivery standards, and adoption work rather than limiting the work to model development.

Employer

WeBuild-AI

Location

London, England, United Kingdom

Work arrangement

Hybrid, with office attendance three days each week

WEBUILD-AI LIMITED is an active private company registered in England. Companies House records its incorporation on 26 March 2024 and classifies its business as information technology consultancy activities.

Agent Frameworks And Client Systems

The role configures and customises Autogen and LangGraph for client applications. The work includes selecting an appropriate orchestration approach, connecting language model services to data sources, and translating a client requirement into an application that can operate securely in production.

Day-to-day technical ownership covers several connected tasks

• Designing and developing AI solutions for enterprise use cases
• Integrating AI capabilities with client systems, digital products, and data sources
• Building and deploying applications that meet performance and security expectations
• Reviewing technical quality across project teams
• Supporting client education and adoption of AI systems

Production AI engineering roles also require testing, monitoring, fault diagnosis, and controlled changes after deployment. The UK National Careers Service describes AI engineering as a combination of model and algorithm development, prototype testing, data analysis, bug fixing, risk assessment, and collaboration during solution rollouts.

Python Cloud And Observability

Strong Python skills are required for the role. The technical stack also calls for experience with AWS or Azure AI services, MLOps, Docker, Kubernetes, vector databases, and the software engineering practices that keep AI applications maintainable.

Langfuse and OpenTelemetry knowledge adds an operational layer to the engineering work. Traces, evaluations, usage signals, and service telemetry help an engineering team inspect how an AI application behaves after release. These controls sit alongside containerisation, cloud services, data access, and model orchestration.

The role's technical requirements include

• Production-grade AI, machine learning, or large language model systems experience
• Autogen and LangGraph experience
• AWS or Azure AI services expertise
• MLOps, Docker, Kubernetes, and vector database knowledge
• Langfuse and OpenTelemetry understanding
• Clear communication and willingness to test new tools and frameworks

Mentoring And The Pathway Platform

The Lead AI Engineer oversees technical quality across project teams and mentors mid-level and junior engineers. The role includes setting engineering expectations, reviewing implementation choices, and helping colleagues turn experimental AI work into dependable client applications.

The role also contributes to the Pathway platform, WeBuild-AI's AI delivery platform, methodology, and services. The position tracks emerging AI tools and frameworks while keeping the team's engineering standards aligned with the systems it builds for clients.

Client Delivery And Cross-Functional Work

WeBuild-AI's engineering teams collaborate with data engineers, cross-functional colleagues, and client stakeholders. The role converts business needs into technical decisions, so communication covers system behaviour, data connections, risk, performance, and the practical steps required for AI adoption.

The London-based delivery includes client-facing problem solving as well as software development. A Lead AI Engineer must explain technical trade-offs to people with different levels of AI experience and keep delivery aligned with the client's operating environment.

Routes Into AI Engineering

The UK National Careers Service recognises university courses and apprenticeships as routes into AI engineering. Relevant degree subjects include artificial intelligence, software engineering, computer science, data science, and mathematics. A related postgraduate qualification can support a move into machine learning work.

The same service identifies Python, SQL, and JavaScript as useful programming languages and lists analytical thinking, systems analysis, computer programming, and attention to detail among the technical foundations. It also records senior roles, team responsibility, project responsibility, consultancy, and self-employment as progression routes for AI engineers.

Additional Context

WeBuild-AI works across financial services, energy and utilities, and retail. Its service scope runs from strategy and governance through data, infrastructure, custom engineering, agents, and automation, which gives the role exposure to different enterprise operating environments.

The employer's careers information records Great Place To Work Certification for April 2026 to April 2027 and describes a team with cross-functional collaboration and dedicated peer support. Those facts sit alongside the role's technical ownership and client delivery responsibilities.