Senior Python Developer – Backend, AIML & 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.