Job Summary :

We are seeking a highly skilled Data Science Lead with hands-on experience in Generative AI, Retrieval-Augmented Generation (RAG), and machine learning model development. The ideal candidate will design scalable AI systems, optimize chunking and retrieval strategies, manage vector databases, and fine-tune large language models (LLMs) for enterprise applications.

Key Responsibilities :

- Lead the design and deployment of RAG-based GenAI systems using LLMs and embeddings

- Develop and optimize chunking strategies for improved retrieval and context relevance

- Fine-tune transformer-based models (e.g., GPT, LLaMA, Mistral) using LoRA, PEFT, or full-model training

- Implement and manage vector databases (e.g., FAISS, Pinecone, Weaviate) for semantic search

- Build and evaluate machine learning models for classification, regression, clustering, and recommendation systems

- Collaborate with engineering teams to integrate ML and GenAI pipelines into production

- Define and monitor performance metrics for model accuracy, latency, and scalability

- Stay updated with the latest research in GenAI, LLMs, and MLOps

- Mentor junior data scientists and contribute to technical leadership

Required Skills & Tools :

- Programming : Python, SQL

- ML Libraries : scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch

- GenAI Frameworks : Hugging Face Transformers, LangChain, OpenAI API

- Vector Databases : FAISS, Pinecone, Weaviate, Qdrant

- LLM Fine-Tuning : LoRA, PEFT, RLHF, prompt tuning

- Data Engineering : Pandas, NumPy, Spark

- Deployment : FastAPI, Docker, Kubernetes, CI/CD

- Cloud Platforms : AWS, GCP, Azure

- Strong problem-solving and communication skills

Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, AI, or related field

- 5 - 7 years of experience in data science, NLP, or machine learning

- Proven track record of building and deploying ML and GenAI applications

Preferred Attributes :

- Experience with open-source LLMs (e.g., Mistral, LLaMA, Falcon)

- Familiarity with semantic search, prompt engineering, and retrieval optimization

- Contributions to AI research or open-source projects

- Certification in Generative AI, NLP, or ML (e.g., DeepLearning.AI, Hugging Face)