Senior AI/ML Engineer

We are looking for a passionate AI/ML Engineer with hands-on experience in building, deploying, and optimizing AI/ML solutions. The ideal candidate should have strong expertise in Python, PyTorch, LangChain, LangGraph, Vector Databases, and Hugging Face Transformers, along with a solid understanding of modern LLM workflows and end-to-end machine learning pipelines. Responsibilities include model development, optimization, agentic AI systems, vector search implementation, data pipeline design, production deployment, evaluation, and collaboration with various teams.

Must 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.

Perks & Benefits

• Premium MacBooks with 22" LED monitors for all team members.
• Office Celebrations (birthdays, milestones).
• Gaming Zone (PlayStation, table tennis).
• Annual 24-hour hackathons.
• Annual Trips.
• Weekly Tech Talks.
• Fixed shifts and a 5-day work week for a healthy lifestyle.
• Work on the latest technologies with world-class clients.
• Company-sponsored health insurance.
• Expert training from top technical experts.
• Free access to courses and books for continuous learning.
• Collaborate on impactful open-source projects.

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.