Key Responsibilities

• Build and optimize classification, regression, and forecasting models using classical ML and deep learning techniques.
• Develop and deploy deep learning architectures including LSTMs, transformers, and other sequence-based models for time-series, NLP, and anomaly detection.
• Design and implement NLP pipelines for text classification, semantic search, summarization, and question answering using transformer-based models (e.g., BERT, T5, GPT).
• Create RAG (retrieval-augmented generation) pipelines integrating LLMs with vector databases (e.g., FAISS, Pinecone, Weaviate) and document indexing frameworks.
• Apply and fine-tune LLMs (e.g., OpenAI, Mistral, LLaMA, Cohere) for domain-specific tasks using supervised fine-tuning or LoRA/QLoRA methods.
• Build and orchestrate multi-agent AI systems using frameworks like LangGraph, CrewAI, or OpenAgents to support tool-using, autonomous agents for decision-making workflows.
• Collaborate with data engineers, product managers, and stakeholders to translate business needs into production-ready solutions.
• Mentor and support junior data scientists through code reviews, model design feedback, and collaborative experimentation.
• Promote best practices in reproducible modeling, responsible AI, and scalable deployment.

Required Skills & Experience

• 5+ years of experience in data science or applied machine learning, with a strong background in both classical and deep learning methods.
• Hands-on experience with Python, and libraries/frameworks such as scikit-learn, pandas, PyTorch, TensorFlow, Hugging Face Transformers, and LangChain.
• Strong understanding of classification metrics, feature engineering, model validation, and hyperparameter tuning.
• Demonstrated experience with LLMs, including fine-tuning, prompt engineering, and retrieval-augmented generation techniques.
• Familiarity with vector databases, embedding models, and chunking strategies for unstructured data (e.g., PDFs, knowledge bases).
• Experience working with multi-agent architectures or orchestration tools like LangGraph, CrewAI, or AutoGPT.
• Solid skills in data analysis, visualization, and communicating technical insights clearly to mixed audiences.
• Knowledge of cloud platforms (AWS/GCP/Azure) and deployment tools (e.g., Docker, MLflow, FastAPI).