Python Developer

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.