- Lead the development of robust ML models across supervised, unsupervised, and time-series problem statements.

- Architect and own scalable data pipelines for feature engineering, model training, evaluation, and monitoring.

- Apply advanced experimentation, error analysis, and model optimization techniques to achieve business KPIs.

- Design and implement GenAI-powered applications such as copilots, document processors, summarization agents, and intelligent assistants.

- Build complex workflows leveraging prompt engineering, RAG pipelines, context management, and hybrid model chaining.

- Lead fine-tuning initiatives using instruction tuning, LoRA, QLoRA, PEFT, or full-model fine-tuning for domain-specific use cases.

- Evaluate trade-offs across open-source and API-based LLMs for performance, cost, and latency optimization.

- Partner with engineering, product, and business teams to shape technical direction and translate insights into deployable solutions.

- Contribute to PoC development, client workshops, and pre-sales discussions as a technical SME.

- Mentor junior and mid-level data scientists, review deliverables, and ensure adherence to MLOps and experimentation best practices.

- Lead internal sessions on the latest advancements in ML/LLM ecosystems and drive innovation within the data science function.

Qualifications & Skills :

- Experience : 6 to 9 years of hands-on experience in Data Science, Machine Learning, or AI product development.

- Programming : Strong proficiency in Python and ML libraries such as pandas, NumPy, scikit-learn, XGBoost, and LightGBM.

- Production ML : Proven experience deploying ML models (batch or real-time) in production-grade environments.

GenAI & LLMs :

- Hands-on experience with OpenAI, Mistral, Claude, LLaMA, or similar models.

- Familiarity with frameworks such as LangChain, LlamaIndex, and Hugging Face Transformers.

- Experience fine-tuning models using PEFT, LoRA, or custom pipelines.

- Vector Databases & Search : Understanding of embeddings, similarity search, and vector databases (e.g., FAISS, Pinecone).

- MLOps & Cloud : Experience with MLflow, SageMaker, Weights & Biases, Docker, Git, and cloud platforms (AWS, GCP, or Azure).

- Bonus : Contributions to open-source projects or demonstrable personal projects in ML or LLMs.

Technical Focus Areas :

- ML/GenAI platform architecture & design

- Model evaluation and safety frameworks for GenAI applications

- Continuous training, deployment, and monitoring best practices