GenAI/Python Engineer - LLM/RAG

Key Responsibilities :

- Design, develop, and deploy AI and Machine Learning solutions using Python.

- Build and optimize Generative AI applications powered by Large Language Models (LLMs).

- Develop intelligent AI agents, chatbots, and automation solutions for enterprise use cases.

- Design and implement Retrieval-Augmented Generation (RAG) pipelines and prompt engineering techniques.

- Fine-tune, evaluate, and optimize machine learning and LLM models for performance and accuracy.

- Integrate AI models with enterprise applications through REST APIs and microservices.

- Collaborate with data scientists, software engineers, and business stakeholders to deliver scalable AI solutions.

- Monitor, troubleshoot, and continuously improve AI model performance in production.

- Follow best practices for AI development, testing, deployment, and documentation.

Required Skills :

- Strong proficiency in Python programming.

- Hands-on experience in Artificial Intelligence (AI) and Machine Learning (ML).

- Experience with Generative AI (GenAI) frameworks and applications.

- Strong understanding of Large Language Models (LLMs) such as GPT, Llama, Claude, or Gemini.

- Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, or Hugging Face Transformers.

- Knowledge of prompt engineering, embeddings, vector databases, and RAG architectures.

- Experience in developing REST APIs and integrating AI solutions with enterprise applications.

- Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization using Docker.

- Strong analytical, problem-solving, and communication skills.

Preferred Skills :

- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel.

- Knowledge of MLOps, CI/CD pipelines, and model deployment.

- Experience with vector databases such as Pinecone, ChromaDB, FAISS, or Milvus.

- Familiarity with Kubernetes, Git, and Agile/Scrum development methodologies.