Python AI Developer (London, ENG)
Summary
Senior Python engineer building backend services, APIs, and agentic GenAI components (LLM orchestration, RAG, evaluation tooling) for an enterprise generative-AI platform serving a large global insurer, using AWS (Bedrock, EKS), FastAPI, and Azure DevOps CI/CD in a regulated environment.
We are seeking a senior and experienced GenAI & Agentic Developer to build core
services, features and capabilities on an enterprise-grade Generative AI Platform for
a large global insurer. This role blends strong Python engineering with DevOps
enablement and hands-on integration of LLM technologies into secure, scalable
enterprise environments.
The ideal candidate will bring practical GenAI & Agentic development experience,
strong automation skills, and the curiosity to solve emerging engineering challenges
in this rapidly evolving space.
Key Responsibilities
- Build and maintain backend services and APIs enabling secure access to LLM
- capabilities.
- Develop and optimise Python-based GenAI & Agentic components including
- prompt orchestration, output validation, and evaluation tooling.
- Integrate LLMs with enterprise systems, observability, and security frameworks.
- Design and maintain CI/CD pipelines aligned to engineering standards (Azure
- DevOps primarily).
- Collaborate closely with platform leads, architects, and SRE teams to ensure
- reliable operationalisation of GenAI services.
- Support benchmarking, evaluation, and experiment tracking for LLM performance
- and cost.
- Contribute to RAG implementations and data access patterns supporting enterprise
- use cases.
- Help shape API standards, reusable patterns, and documentation for platform
- adoption.
- Troubleshoot and optimise performance across distributed systems and cloud
- services.
- Mandatory Skills & Experience
- Strong Python backend engineering in production systems (inc FastAPI).
- AWS services: EC2, EKS, S3, SQS, Kenesis/Firehose, Bedrock and AgentCore.
- Working knowledge of GenAI technologies and Large Language Models.
- Experience evaluating LLM performance and prompt handling complexities.
- Solid DevOps mindset with CI/CD expertise and observability best practices.
- Comfortable working in regulated enterprise environments with strict security
- controls.
- Experience integrating AI services into real-world apps or workflows.
- Knowledge of authentication, secret management, network boundaries, and model
- access governance
- Preferred Skills
- Kong API Gateway, Kong Mesh, Flux CD.
- RESTful API development (FastAPI preferred), microservices, Terraform with
- GitOps workflows.
- IaC & Terraform experience with GitOps workflows.
- Prompt redteaming & evaluation tools (like Promptfoo & Arize).
- Experience with SQL (MySQL, PostgreSQL) and NoSQL (MongoDB, Cassandra)
- databases
- Exposure to RAG patterns and vector search technologies.
- Behavioural Competencies
- Proactive self-starter who identifies gaps and drives solutions without waiting for
- tickets.
- Comfortable working in ambiguous emerging domains where best practice is
- evolving.
- Collaborative communicator able to influence and gain trust across engineering,
- data, and product teams.
- Curiosity to explore and adopt new GenAI techniques in a structured, secure way.
- Bias toward automating everything to reduce toil and accelerate delivery.
- What Success Looks Like
- Efficient, secure, and reusable GenAI components delivered into production.
- Improved engineering velocity through automation and DevOps best practices.
- Stable services, with observability and evaluation built in from day one.
- Platform teams and application teams love using what you build.
- Clear documentation and fast onboarding for future use cases.
Skills
- Agentic AI
- AI
- API
- API Gateway
- Authentication
- Automation
- AWS
- Azure
- Azure DevOps
- Cassandra
- CI/CD
- Cloud
- DevOps
- Distributed Systems
- EC2
- EKS
- FastAPI
- Flux
- Generative AI
- GitOps
- Infrastructure as Code
- LLM
- Microservices
- MongoDB
- MySQL
- NoSQL
- Observability
- PostgreSQL
- Python
- RAG
- REST
- S3
- SQL
- SQS
- SRE
- Terraform
- Vector Search