Senior Python Developer Backend, AI/ML & RAG

Experience: 8+ years in software development, with strong Python backend expertise and hands-on experience delivering AI/ML applications.

Our team builds enterprise applications that combine robust backend engineering with AI-powered experiences. We develop intelligent assistants, knowledge-retrieval solutions, and workflow automation that integrate with enterprise platforms and services.

We are looking for a senior developer who can translate business requirements into secure, scalable applications, guide technical design, and take ownership from development through production support.

• Design, develop, and maintain scalable Python backend services, REST APIs, and service-to-service integrations.


• Build AI-powered applications using large language models (LLMs), agent frameworks, and machine learning capabilities.


• Develop end-to-end Retrieval-Augmented Generation (RAG) pipelines covering document ingestion, chunking, embeddings, indexing, retrieval, and grounded response generation.


• Implement prompt engineering, agent workflows, tool calling, and Model Context Protocol (MCP) integrations.


• Integrate enterprise knowledge sources and applications, including ITSM platforms, Confluence, SharePoint, and enterprise APIs.


• Evaluate AI solutions for accuracy, groundedness, hallucinations, retrieval quality, latency, and cost.


• Build automated functional, integration, regression, performance, and AI evaluation tests.


• Lead technical design discussions and code reviews; document architecture, APIs, and implementation decisions.


• Troubleshoot complex application, API, integration, and production issues, and implement sustainable fixes.


• Improve application performance, reliability, observability, and model/token efficiency.


• Apply secure coding practices, access controls, privacy safeguards, and enterprise AI governance requirements.


• Collaborate with product, QA, engineering, and business stakeholders to define requirements and delivery priorities.


• Mentor engineers and improve development practices across the software development lifecycle.


• Support production operations, dependency upgrades, vulnerability remediation, and application maintenance.

• Python and backend engineering: Strong hands-on Python expertise, including asynchronous programming, exception handling, testing, and maintainable application design.


• API development: Experience building production APIs using FastAPI, Flask, Django, or a comparable framework; strong understanding of authentication, authorization, versioning, and error handling.


• Backend architecture: Experience designing scalable services, managing concurrency, and implementing caching, background processing, retries, and resilient integrations.


• Data management: Strong SQL and relational database skills, with experience in data modelling, query optimisation, and application data access.


• LLM application development: Hands-on experience integrating LLMs into applications, including prompt design, structured outputs, tool calling, and failure handling.


• RAG and retrieval: Practical experience with embeddings, vector databases or vector search, chunking strategies, metadata filtering, and retrieval optimisation.


• ML fundamentals: Understanding of model inference, data preparation, evaluation metrics, and the distinction between model quality and application performance.


• AI evaluation: Ability to build repeatable evaluations for retrieval relevance, response quality, groundedness, latency, and cost.


• Security and privacy: Experience protecting sensitive data, managing secrets, enforcing access controls, and addressing prompt injection and unsafe tool execution.


• Production delivery: Experience with cloud deployment, containers, CI/CD, logging, monitoring, and production troubleshooting.


• Technical leadership: Ability to lead design and code reviews, mentor engineers, and communicate technical decisions clearly.

• Experience with OCI or other major cloud platforms.


• Experience with agent orchestration frameworks such as LangChain, LangGraph, or equivalent tools.


• Hands-on experience building or integrating MCP servers and clients.


• Experience with hybrid search, reranking, query rewriting, and permission-aware enterprise retrieval.


• Experience with Slack APIs, Jira Service Management, chatbots, and IT service-management workflows.


• Familiarity with proactive support, ticket routing, and self-healing automation.


• Experience with Kubernetes, infrastructure as code, and distributed tracing.


• Experience with MLOps/LLMOps, model adaptation or fine-tuning, and model-serving platforms.


• Familiarity with AI auditability, data residency, and enterprise governance controls.