Lead AI Engineer – Associate Director
Your role will be to own the AI engineering strategy, supporting our Capital Markets clients to design, implement and operate scalable AI solutions across their businesses. You will be an exceptional communicator, able to articulate complex technical concepts to both technical and non‑technical stakeholders, collaborate with cross‑functional teams and deliver high‑quality solutions that meet or exceed client expectations.
Key Responsibilities
- Translating the vision of senior client stakeholders into a delivery and implementation roadmap, ensuring alignment with strategic goals and digital transformation efforts. At the same time, providing recommendations on the suitability of AI/ML initiatives and use cases for scaling across our client organisations.
- Designing, recommending, and implementing end‑to‑end AI solutions that seamlessly integrate with existing client systems.
- Collaborating with Enterprise, Application, Data, and DevOps Architects, Data Scientists, MLOps and GenAI Architects, and Business teams to pilot use cases and discuss engineering design and implementation.
- Selecting appropriate technologies from a pool of open‑source and commercial offerings, considering deployment models and integration with existing tools.
- Being responsible for the successful execution and operational improvement of AI‑powered applications using agile methodology.
- Working closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
- Developing and maintaining contact with top decision makers, lead proposal development, and contribute to pricing strategies.
- Managing diverse teams within an inclusive team culture where people are recognised for their contribution and developing the capability of junior team members through on‑the‑job training and formal development programmes.
Qualifications
- Software/data engineering experience with a focus on applied AI engineering, using Python and SQL (ML engineering experience preferred).
- Experience across the Financial Services industry, with a primary focus on the Capital Markets sector.
- Experience designing Agentic AI solutions, managing and sequencing scope, estimating the build effort and dependencies, and thinking commercially about AI systems to estimate or measure return on investment.
- Exposure to software delivery methodologies and tooling – Agile/SaFe, Extreme Programming, Jira, Confluence, Linear, Monday.
- Experience with LLM fundamentals: prompt engineering, fine‑tuning, embedding models and RAG patterns.
- Experience working with at least one vector database product (e.g., Pinecone), an agent framework (e.g., LangChain, LangGraph, Agent Development Kit), and MCP.
- Knowledgeable in designing and building evaluation frameworks for agentic systems and comfortable building API‑enabled backend services (e.g., FastAPI).
- Strong understanding of modern data architectures, and ability to proactively identify technical and delivery risks.
- Comfortable using common CI/CD tooling, including setting up CI/CD pipelines for ML engineering or agent development.
- Knowledge and experience in MLOps or LLMOps.
- Experience working with at least one hyperscaler stack, cloud certifications preferred (AWS, Azure, GCP, or Databricks).
- Senior stakeholder management skills and ability to collaborate effectively with multidisciplinary teams.
- Ability to bring teams together and lead technical programmes to drive success through coaching, facilitation, stakeholder management and expectation management capabilities.
- Strong communication and collaboration abilities, with capability to work effectively with cross‑functional teams and stakeholders.
- Experience leading go‑to‑market activities such as bids, responding to RFPs, and developing high‑quality proposal materials.
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