Description :

Key Responsibilities :

- Design, develop, and maintain scalable backend applications using Python, with a strong emphasis on Generative AIdriven solutions, ensuring high performance, reliability, and maintainability across environments.

- Actively contribute to the end-to-end development of GenAI features, including prompt engineering, orchestration of LLM workflows, response evaluation, fine-tuning strategies, and integration of AI outputs into production-grade applications.

- Build, optimize, and manage RESTful APIs using Django and Django REST Framework (DRF) to support AI-powered services, frontend applications, third-party integrations, and internal platforms.

- Integrate Large Language Models (LLMs) such as OpenAI, Anthropic, Azure OpenAI, or open-source models (e.g., LLaMA, Mistral) into backend systems, ensuring efficient token usage, latency optimization, and secure API handling.

- Develop and manage AI pipelines that include data ingestion, preprocessing, embedding generation, vector storage, retrieval-augmented generation (RAG), and post-processing of AI-generated responses.

- Implement vector databases and semantic search solutions (e.g., FAISS, Pinecone, Weaviate, Chroma) to enable contextual retrieval, document-based Q&A systems, and conversational AI experiences.

- Collaborate closely with product managers, data scientists, and frontend engineers to translate business requirements into robust AI-backed APIs and backend services.

- Design and implement authentication, authorization, and role-based access control mechanisms within Django applications to ensure secure access to AI features and sensitive data.

- Ensure backend systems comply with data privacy, security, and compliance standards, especially when handling user-generated content and AI-processed data.

- Optimize application performance by profiling Python code, improving database queries, caching AI responses where applicable, and managing concurrency for high-throughput API calls.

- Build and maintain asynchronous and background processing workflows (e.g., Celery, Redis, RabbitMQ) for long-running AI tasks such as document processing, model inference, and batch prompt execution.

- Write clean, reusable, and testable code following Python best practices, design patterns, and coding standards, ensuring long-term maintainability of AI and non-AI components.

- Develop comprehensive unit tests, integration tests, and API tests to validate Django APIs, GenAI workflows, and edge cases related to AI-generated outputs.

- Monitor, log, and troubleshoot production issues related to backend services and AI integrations, ensuring minimal downtime and proactive issue resolution.

- Participate in model evaluation and iteration, analyzing AI outputs for quality, bias, hallucination risks, and relevance, and continuously improving prompt strategies and system design.

- Work with databases such as PostgreSQL/MySQL and NoSQL solutions where required, designing schemas that support AI metadata, embeddings, logs, and conversational history.

- Contribute to API documentation (Swagger/OpenAPI) and internal technical documentation to ensure clarity and ease of integration for internal and external consumers.

- Support deployment and CI/CD processes by collaborating with DevOps teams, ensuring smooth rollout of Django and AI services across staging and production environments.

- Stay updated with the latest advancements in Generative AI, Python frameworks, Django ecosystem, and backend best practices, and proactively suggest improvements or new approaches.

- Assist in mentoring junior developers by providing guidance on Python, Django REST APIs, and GenAI concepts, promoting best practices across the team.

- Take ownership of assigned modules or services, ensuring timely delivery, high quality, and alignment with overall system architecture and business goals.