We are seeking an experienced GenAI / AI-ML Engineer to design, develop, and deploy scalable Generative AI and Machine Learning solutions. In this role, you will build modern RAG pipelines, deploy multi-agent workflows, fine-tune models, and integrate scalable backend APIs for enterprise-grade applications.
Key Responsibilities
- GenAI & Agentic Systems: Develop LLM-powered applications utilizing prompt engineering, AI agents, and multi-agent workflows (LangChain, LangGraph).
- RAG Pipelines: Build, optimize, and evaluate Retrieval-Augmented Generation (RAG) pipelines using modern vector databases and frameworks like RAGAS.
- Backend Integration: Write robust Python code and build high-performance REST APIs using FastAPI to support AI models in production.
- ML/DL Engineering: Design end-to-end data preprocessing, feature engineering, fine-tuning, and model evaluation pipelines using Scikit-learn, PyTorch, TensorFlow, and Keras.
- Cloud & DevOps: Containerize applications using Docker and deploy scalable models on AWS (SageMaker, Bedrock, EC2, S3) with CI/CD integration.
- Collaboration: Partner with Data Engineering, DevOps, and Product teams to ensure reliability, security, and performance of production environments.
Requirements
- Experience: 4–6 years in core Machine Learning / Deep Learning, with at least 1+ years of dedicated hands-on experience in GenAI/LLM projects.
- Programming & Backend: Strong proficiency in Python, SQL, and framework expertise with FastAPI.
- GenAI Stack: Hands-on experience with OpenAI APIs, Hugging Face, LangChain, LangGraph, LangSmith, and vector databases (Pinecone, FAISS).
- Evaluation Metrics: Practical experience with evaluation frameworks (RAGAS, BLEU, ROUGE) and semantic search architectures.
- Cloud Infrastructure: Solid background with AWS services (EC2, S3, SageMaker, Bedrock), Docker, and Git.
