Python AI Developer

Sandhata Technologies Limited; Greater London; On-site; GBP 110,000 - 170,000; Full time

Job summary

Sandhata Technologies Limited is seeking a senior GenAI & Agentic Developer to build core services on an enterprise-grade Generative AI Platform for a large insurer. You will blend Python engineering with DevOps enablement and hands-on LLM integration in secure, scalable environments.

The role requires practical GenAI development, strong automation skills, and curiosity to solve evolving engineering challenges. You will contribute to API standards and reusable patterns for platform adoption.

Qualifications

• Production Python backend with FastAPI experience in enterprise systems.
• Hands-on GenAI/LLM integration and evaluation.
• Strong DevOps mindset with CI/CD and observability best practices.

Responsibilities

• Build and maintain backend services and APIs for secure access to LLM capabilities.
• Develop Python-based GenAI components including prompt orchestration and evaluation tooling.
• Integrate LLMs with enterprise systems, observability, and security frameworks.

Skills

Python backend

FastAPI

GenAI experience

LLM evaluation

CI/CD automation

Security compliance

Observability

Tools

Kong API Gateway

Kong Mesh

Flux CD

Terraform

GitOps

Job description

Overview

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 Back End 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 Back End 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.