Lead AI Engineer
- killamarsh, england, United Kingdom
- Job type not listed
- £120 - £180 Per Day
Job Description
Role Overview
We are seeking a highly experienced Lead AI Platform Engineer to drive the engineering and implementation of AI capabilities within a Finance AI platform. This is a hands-on technical leadership role focused on establishing scalable AI engineering patterns, accelerating delivery, and mentoring development teams. The ideal candidate will combine strong cloud-native engineering expertise with practical experience building and integrating GenAI and Agentic AI solutions into enterprise platforms.
Lead AI Platform Engineer
We are seeking a highly experienced Lead AI Platform Engineer to drive the engineering and implementation of AI capabilities within a Finance AI platform. This is a hands-on technical leadership role focused on establishing scalable AI engineering patterns, accelerating delivery, and mentoring development teams. The ideal candidate will combine strong cloud-native engineering expertise with practical experience building and integrating GenAI and Agentic AI solutions into enterprise platforms.
Experience: 12+ Years
Key Responsibilities
- Lead the implementation of AI-enabled applications and platform capabilities.
- Establish reusable patterns, frameworks, and best practices for AI engineering.
- Design and oversee LLM, RAG, and Agentic AI integrations within enterprise environments.
- Collaborate with architects and engineering teams to ensure scalable, secure, and maintainable solutions.
- Define standards for AI observability, governance, security, and performance.
- Mentor engineers and provide technical leadership across AI development initiatives.
- Contribute hands-on to solution design, development, code reviews, and production deployment.
Required Skills
- 12+ years of software engineering experience, with strong expertise in Java and/or Python.
- Proven experience building cloud-native applications on Azure, AWS, or GCP.
- Strong knowledge of Kubernetes, Microservices, APIs, Event-Driven Architecture, and DevOps practices.
- Hands-on experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions.
- Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent.