About the role
Responsibilities:
- Build and deploy Generative AI solutions using LLMs (GPT, Llama, Claude, Gemini, etc. ).
- Develop prompt engineering, prompt optimization, and retrieval strategies (RAG pipelines).
- Fine-tune open-source LLMs using LoRA, parameter-efficient tuning, or custom datasets.
- Design and develop end-to-end GenAI applications leveraging cloud AI services (Azure/OpenAI, AWS Bedrock, and GCP Vertex AI).
- Implement Vector DB-based retrieval systems using FAISS / Pinecone / Chroma / Milvus.
- Build AI agents, chatbots, copilots, and automation workflows.
- Work closely with Data Engineering and MLOps teams for model deployment and monitoring.
- Ensure data privacy, security, and governance in AI models and pipelines.
- Evaluate new models, architectures, and AI frameworks; perform POCs and feasibility studies.
Requirements:
- Strong expertise in LLMs, Transformers, OpenAI APIs, HuggingFace, LangChain, or LlamaIndex.
- Experience with Prompt Engineering and RAG (Retrieval-Augmented Generation).
- Hands-on experience in Python and PyTorch / TensorFlow.
- Experience deploying AI models using Azure / AWS / GCP.
- Knowledge of Vector Databases (FAISS, Pinecone, Chroma, Milvus).
- Experience with ML workflows, scalable API development, and model deployment.
Good-to-Have Skills:
- Experience fine-tuning foundation models (Llama 2/3 Falcon, Mistral, etc. ).
- Knowledge of MLOps pipelines using MLflow / Kubeflow / Azure ML.
- Experience in NLP, embeddings, text generation, and conversational systems.
- Understanding of Docker, Kubernetes, CI/CD pipelines.
- Experience building enterprise-grade chatbots or AI agents.