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.