Our client is a global consulting firm building and deploying production AI systems for major banking clients. This role sits at the intersection of engineering and delivery — designing scalable agentic solutions, leading technical implementation, and owning the path from prototype to production. You'll work across the full stack: LLM architecture, RAG pipelines, MLOps, CI/CD, and regulatory compliance, collaborating with data scientists, architects, and senior client stakeholders to ship AI that works at enterprise scale.
Core requirements
- Proven software or data engineering background, with a track record of applied AI delivery using Python and SQL
- Experience in banking or broader financial services
- Solid grasp of LLM fundamentals — prompt engineering, fine-tuning, embedding models, and RAG patterns
- Hands-on experience with at least one vector database (e.g. Pinecone) and at least one agent framework (e.g. LangChain, LangGraph, or Agent Development Kit)
- Ability to design and build evaluation frameworks for agentic systems
- Comfortable building API-enabled backend services, e.g. FastAPI
- MLOps or LLMOps knowledge, including CI/CD pipeline setup for ML or agent development
- Experience with at least one hyperscaler stack — AWS, Azure, GCP, or Databricks; cloud certifications preferred
- Familiarity with Agile or SaFe delivery methodologies
The role also requires
- Senior stakeholder management — translating technical concepts clearly for non-technical audiences
- Experience scoping and estimating agentic AI builds, including commercial thinking around ROI
- Confidence contributing to bids, RFPs, and proposal development
- Experience managing and developing junior team members
Client-facing and delivery-led. You'll manage teams, shape proposals, and own outcomes.
”, “datePosted”: “2026-05-20”, “hiringOrganization”: { “@type”: “Organization”, “name”: “Milburn Lewis”, “sameAs”: “https://uk.whatjobs.com/pub_api__cpl__436960268__4861?utm_campaign=publisher&utm_medium=api&utm_source=4861&geoID=33” }, “jobLocation”: { “@type”: “Place”, “address”: { “@type”: “PostalAddress”, “addressLocality”: “London” } } }Our client is a global consulting firm building and deploying production AI systems for major banking clients. This role sits at the intersection of engineering and delivery — designing scalable agentic solutions, leading technical implementation, and owning the path from prototype to production. You’ll work across the full stack: LLM architecture, RAG pipelines, MLOps, CI/CD, and regulatory compliance, collaborating with data scientists, architects, and senior client stakeholders to ship AI that works at enterprise scale.
Core requirements
- Proven software or data engineering background, with a track record of applied AI delivery using Python and SQL
- Experience in banking or broader financial services
- Solid grasp of LLM fundamentals — prompt engineering, fine-tuning, embedding models, and RAG patterns
- Hands-on experience with at least one vector database (e.g. Pinecone) and at least one agent framework (e.g. LangChain, LangGraph, or Agent Development Kit)
- Ability to design and build evaluation frameworks for agentic systems
- Comfortable building API-enabled backend services, e.g. FastAPI
- MLOps or LLMOps knowledge, including CI/CD pipeline setup for ML or agent development
- Experience with at least one hyperscaler stack — AWS, Azure, GCP, or Databricks; cloud certifications preferred
- Familiarity with Agile or SaFe delivery methodologies
The role also requires
- Senior stakeholder management — translating technical concepts clearly for non-technical audiences
- Experience scoping and estimating agentic AI builds, including commercial thinking around ROI
- Confidence contributing to bids, RFPs, and proposal development
- Experience managing and developing junior team members
Client-facing and delivery-led. You’ll manage teams, shape proposals, and own outcomes.
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