Senior AI/ML Engineer

Job Description

About This Position The ideal candidate should have strong expertise in Python, PyTorch, LangChain, LangGraph, Vector Databases, and Hugging Face (HF) Transformers, along with a solid understanding of modern LLM workflows and end-to-end machine learning pipelines. What are you going to do? Model Development: Design, build, and deploy AI/ML models, including deep learning and LLM-based solutions. Optimization: Develop, fine-tune, and optimize models using PyTorch and HF Transformers. Agentic AI & Workflows: Architect and build Agentic AI systems, autonomous agents, and complex RAG workflows using LangChain and LangGraph. Vector Search: Implement and manage Vector Databases (Pinecone, FAISS, Chroma, Weaviate, etc.) for embedding storage and retrieval. Data Pipelines: Work with large datasets to perform data preprocessing, feature engineering, and data pipeline design. Production Deployment: Integrate ML models into production using scalable architectures and APIs (FastAPI / Flask). Evaluation: Perform model evaluation, benchmarking, and optimization for performance and accuracy. Collaboration: Collaborate with product, data, and engineering teams to translate requirements into effective AI solutions. Continuous Learning: Stay updated with emerging AI/ML advancements, frameworks, and best practices You Need To Have Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, or a related field. 3+ years of experience as an AI/ML Engineer, ML Researcher, or Deep Learning Engineer. Strong programming skills in Python and experience with ML frameworks like PyTorch. Experience working with Hugging Face Transformers for model training and fine-tuning. Strong understanding of LLM fine-tuning, RAG architectures, prompt engineering, and model evaluation. Hands-on experience with LangChain and LangGraph in building conversational AI, agents, or workflow-based solutions. Experience with MLOps tools and version control systems like MLflow, DVC, and Airflow. Good knowledge of cloud ecosystems (AWS, GCP, Azure) and containerization (Docker). Experience with API development, preferably using FastAPI or Flask.