Our client, a global organisation, urgently require an experienced AI Delivery Lead to join their expanding team on a permanent basis.
In order to be successful, you will have the following experience:
- Strong background within leading end to end implementation of AI/ML and Generative AI solutions
- Experience within pre-sales, sales or consulting
- Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services
- Experience of managing teams of 15-20 people
- Ideally you will have strong technical understanding within Python, PyTorch, TensorFlow, and specialised Generative AI libraries
- SC Cleared is essential as a minimum
Within this role, you will be responsible for:
- Determine where AI adds genuine value and where simpler, traditional engineering approaches are more effective for the client’s mission.
- Convert high-level client requirements into detailed technical roadmaps and actionable engineering tasks.
- Proactively identify and manage technical risks, including security vulnerabilities and deployment bottlenecks to drive timely delivery.
- Shift AI from experimental prototypes to hardened, production-ready services that meet the high security and reliability standards of our clients.
- Oversee technical teams throughout delivery, ensuring that engineering efforts align with broader project goals and delivery timelines.
- Communicate complex technical concepts to non-technical senior stakeholders, building confidence in AI-driven transformations.
- Lead and upskill cross-functional teams of data scientists and engineers, fostering a culture of innovation and engineering excellence.
- Design scalable, secure, and production-ready architecture that integrate LLMs and ML models into complex enterprise workflows.
- Oversee the full technical lifecycle, from selecting the right model architecture to ensuring robust CI/CD and MLOps pipelines.
- Deploy and optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.
- Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part of the MLOps lifecycle.
- Assess and optimise for performance, cost-efficiency, and reliability, ensuring AI outputs meet the rigorous standards required for our Security & Justice clients.
- Design and implement comprehensive evaluation, monitoring, and observability frameworks to track AI performance and system health in real-time.
This represents an excellent opportunity to secure a permanent role within a high profile organisation