Key Responsibilities:

- Design and develop end-to-end AI/GenAI applications for enterprise use cases.

- Build and deploy solutions leveraging LLMs, RAG, prompt engineering, AI agents, and foundation models.

- Design AI application architecture covering data ingestion, model orchestration, APIs, business logic, and user interfaces.

- Develop scalable backend services and AI APIs using Python, REST APIs, microservices, and cloud-native technologies.

- Implement RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.

- Work with vector databases and enterprise data sources to build reliable knowledge retrieval solutions.

- Integrate AI services with frontend applications and existing enterprise platforms.

- Develop AI solutions using cloud ecosystems such as Azure, AWS, or GCP.

- Build and maintain deployment pipelines for AI applications using CI/CD, containers, and DevOps/MLOps practices.

- Optimize AI applications for performance, scalability, reliability, security, and cost.

- Collaborate with data engineers, software engineers, product teams, architects, and business stakeholders.

- Contribute to technical solutioning, POCs, prototypes, and production implementations.

- Establish best practices for AI application development, testing, monitoring, and governance.

Required Skills & Experience :

- Strong hands-on experience in Python and AI application development.

- Strong understanding of LLMs, Generative AI, RAG, embeddings, prompt engineering, and foundation models.

- Experience with AI system design and solution architecture.

- Experience building enterprise-grade AI applications from concept to production.

- Strong experience with REST APIs, microservices, backend development, and API integration.

- Experience with vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, Milvus, or equivalent.

- Strong SQL and data-handling capabilities.

- Experience working with at least one major cloud platform: Azure, AWS, or GCP.

- Understanding of CI/CD, Docker, Kubernetes, DevOps/MLOps, and cloud deployment practices.

- Experience integrating AI/backend services with frontend applications.

- Understanding of AI application security, scalability, observability, and performance optimization.