Lead AI Engineer / Lead AI Solution Architect (Ref 26286) - #1133404

Responsibilities

• Lead the end to end technical design of AI and Agentic AI solutions, ensuring alignment with enterprise architecture, security controls, and platform standards.

• Translate high level requirements from architects and product owners into detailed technical specifications, development tasks, and implementation plans.

• Oversee the full development lifecycle for AI, software, and automation workflows, providing hands on guidance and removing technical blockers for the team.

• Provide technical leadership across backend, frontend, API integration, LLM orchestration, agent frameworks, and workflow automation to ensure consistency and quality.

• Review and approve solution designs, architecture diagrams, data flows, and code contributions from senior and junior engineers.

• Drive the integration of AI solutions with enterprise systems, ensuring robust connectivity through APIs, MCP and other frameworks, while enforcing best practices.

• Guide the design of hybrid solutions that combine AI models, prompting logic, rule engines, middleware, RPA, and human in the loop elements in a coherent workflow.

• Oversee the stability and performance of production systems, define monitoring strategies, and ensure issues are escalated and resolved quickly.

• Establish engineering standards covering code quality, documentation, testing strategies, CI and CD practices, and security reviews.

• Mentor and coach senior and junior AI engineers, provide structured guidance, and drive upskilling for full stack development, AI integration, and delivery discipline.

• Work closely with platform teams, cybersecurity, data engineering, product owners, and business stakeholders to ensure alignment and timely delivery.

• Own risk identification and mitigation across AI solutions, including fallback strategies, model behaviour oversight, and protection against misuse or unexpected outputs.

• Represent the AI engineering team in architecture discussions, solution reviews, and governance forums.

• Able to design and maintain RAG systems, including content ingestion, embedding, vector storage, retrieval tuning, and accuracy checks for enterprise use cases.

• Drive continuous improvement for the marketplace recommender engine, enterprise AI services, and core reusable components.

• Coordinate effort estimation, sprint planning, prioritisation, and resource allocation for the AI engineering team.

• Ensure delivered solutions are secure, scalable, compliant, and ready for production deployment.

• Keeping close watch on new developments in AI, running small proof of concept work to understand their value, and advising the team on which solutions are suitable for adoption.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence or a related technical field. Candidates with advanced specialisation in full stack engineering or AI engineering are preferred.

• More than 7 years of hands on experience delivering full stack software and AI solutions, with at least 2 to 3 years in a senior or technical lead capacity supervising engineers. Proven experience designing end to end AI and Agentic AI solutions OR Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.

• Strong full stack engineering capability across frontend, backend, API design and system integration, with deep proficiency in Python, TypeScript or NodeJS and SQL.

• Experienced in designing and leading implementation of LLM based and agentic AI systems, including orchestration patterns using frameworks such as LangChain or LangGraph.

• Strong capability in integrating AI solutions with enterprise platforms, covering RESTful APIs, gRPC, MCP and secure middleware components.

• Solid understanding of authentication and authorization patterns including OAuth2, JWT and SSO technologies.

• Hands on experience designing scalable cloud deployments on Azure, AWS or GCP with good understanding of CI and CD practices.

• Proven ability to conduct technical reviews, enforce engineering standards, and guide other engineers in code quality, architecture and system performance.

• Proven ability to troubleshoot complex issues across frontend, backend, APIs, AI models and middleware in production environments.

• Experience in LLMOps/GenAIOps, MLOps, DevSecOps

• Passion for applied R&D, staying ahead of AI advancements, and bringing innovative ideas into production.

• Strong problem-solving ability, attention to detail, and ability to collaborate effectively across teams.

• Capable to work in a cross-functional team (Software/AI engineers, data platform, business users).

Licence no: 12C6060