Data Scientist

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.

Strong 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.