Role Overview

As a L3 Data Scientist, you will lead the design, development, and deployment of advanced machine learning and GenAI applications. You will be responsible for end-to-end ownership of model development, fine-tuning LLMs, and building intelligent systems that integrate seamlessly into client products. This role combines deep technical expertise, product thinking, and the ability to mentor and influence others.

This is a senior individual contributor role, ideal for professionals who thrive on solving ambiguous, high-impact problems using a blend of classical ML and cutting-edge LLM technologies.

Key Responsibilities

ML & Data Science Leadership

- Lead the development of robust machine learning solutions across supervised, unsupervised, and time-series problems.

- Architect and own pipelines for feature engineering, training, evaluation, and monitoring of models.

- Apply advanced experimentation techniques, error analysis, and iterative improvements to meet business KPIs.

LLM & GenAI System Development

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

- Build advanced workflows using prompt engineering, RAG pipelines, context management, and hybrid model chaining.

- Lead LLM fine-tuning efforts using instruction tuning, parameter-efficient methods (LoRA, QLoRA, PEFT), or full-model fine-tuning for domain-specific use cases.

- Evaluate trade-offs across LLM providers (open-source vs API-based) and optimize for performance, cost, and latency.

Collaboration & Impact

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

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

- Drive technical quality,



review deliverables, and ensure adherence to best practices across the data science lifecycle.

Mentorship & Technical Leadership

- Mentor junior and mid-level data scientists on modeling approaches, GenAI architecture, and clean experimentation workflows.

- Lead internal sessions on advancements in ML/LLM ecosystems and guide capability development within the team.

Required Skills & Qualifications

- 6–9 years of hands-on experience in data science, machine learning, or AI product development.

- Robust Python programming skills with expertise in libraries like pandas, NumPy, scikit-learn, XGBoost/LightGBM.

- Proven track record of shipping ML models to production (batch or real-time) in business-critical applications.

- Hands-on experience working with LLMs (OpenAI, Mistral, Claude, LLaMA) and frameworks like LangChain or LlamaIndex.

- Direct experience in fine-tuning LLMs using Hugging Face Transformers, PEFT, or custom pipelines.

- Strong understanding of prompt engineering, embeddings, similarity search, and vector databases (e.g., FAISS, Pinecone).

- Experience with Docker, Git, and cloud platforms (AWS, GCP, or Azure).

Good-to-Have

- Exposure to ML/GenAI platform design and MLOps tools (e.g., MLflow, SageMaker, Weights & Biases).

- Experience with evaluation and safety frameworks for GenAI applications.

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

What You’ll Love at here

- Solve real-world problems with ML and GenAI, not just toy models or prototypes.

- Collaborate with talented engineers, designers, and product thinkers in a flat, high-ownership environment.

- Work across industries and domains while experimenting with the latest in LLMs and AI tooling.

- Shape the future of AI development within a fast-growing, impact-focused team.

📌 Data Scientist (Bengaluru)
🏢 CoffeeBeans
📍 Bengaluru