- Design, develop, and deploy scalable backend services for Generative AI applications.

- Build intelligent applications using Large Language Models (LLMs) such as GPT, Claude, Llama, or similar foundation models.

- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise AI use cases.

- Integrate vector databases to enable semantic search, knowledge retrieval, and AI-driven recommendations.

- Implement AI orchestration workflows using frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

- Design and develop RESTful APIs and microservices for AI-powered products.

- Optimize AI inference performance, latency, scalability, and operational efficiency.

- Deploy and manage AI applications using Docker, Kubernetes, and cloud platforms such as AWS, Azure, or Google Cloud.

- Collaborate with product managers, data scientists, and frontend developers to deliver end-to-end AI solutions.

- Ensure software quality through unit testing, code reviews, CI/CD pipelines, and DevOps best practices.

- Monitor application performance, troubleshoot production issues, and continuously improve system reliability.

- Stay updated with the latest advancements in Generative AI, LLMs, and emerging AI frameworks.

Required Skills & Qualifications :

- 5 - 9 years of experience in backend software development with strong expertise in Python.

- Hands-on experience building production-grade applications using Large Language Models (LLMs).

- Strong understanding and practical implementation of Retrieval-Augmented Generation (RAG).

- Experience working with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS.

- Expertise in AI orchestration frameworks including LangChain, LlamaIndex, or Semantic Kernel.

- Strong knowledge of SQL and database optimization.

- Experience designing REST APIs and microservices using frameworks such as FastAPI, Flask, or Django.

- Hands-on experience with Docker, Kubernetes, and containerized deployments.

- Experience working with AWS, Microsoft Azure, or Google Cloud Platform.

- Knowledge of distributed systems, scalable backend architecture, and cloud-native application development.

- Experience with Git, CI/CD pipelines, and Agile software development methodologies.

- Excellent analytical, debugging, communication, and problem-solving skills.

Preferred Qualifications :

- Experience deploying and monitoring LLM applications in production environments.

- Knowledge of prompt engineering, AI agents, autonomous workflows, and function calling.

- Familiarity with MLOps practices, model evaluation, observability, and AI governance.

- Experience integrating third-party AI APIs and enterprise data sources.

- AWS, Azure, or Google Cloud certifications are an added advantage.

Education :

- Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, Artificial Intelligence, or a related discipline.