Senior Data Scientist

Rocket Learning; Bengaluru; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

Rocket Learning is seeking a senior ML/AI engineer to design and scale AI-powered learning systems in Bengaluru. You will lead the development and deployment of ML/LLM-based solutions that personalize learning, assist teachers, and enhance programs across the organization.

Ideal candidates have 3+ years building production AI/ML systems, strong Python and ML tool experience, and a track record of collaborating with product and engineering teams to ship reliable, scalable solutions.

Qualifications

• 3+ years of hands-on experience building and deploying AI/ML systems in production.
• Strong experience in applied ML, NLP, LLMs, and modern AI workflows.
• Strong fundamentals in statistics, ML algorithms, deep learning, and system design.
• Proficiency in Python and ML ecosystem tools (pandas, numpy, scikit‑learn, PyTorch/TensorFlow).
• Experience with ML workflow tools such as MLflow, experiment tracking, and model registries.
• Hands‑on experience with LLMs, fine‑tuning, embeddings, vector databases, and RAG pipelines.
• Experience deploying models/services through APIs, cloud infrastructure, or containerized workflows.
• Strong collaboration and stakeholder management skills across product and engineering teams.

Responsibilities

• Design, architect, and scale AI/ML and LLM-powered systems that drive personalization, assist teachers, and improve learning programs.
• Lead the design, development, and deployment of ML, deep learning, and LLM-based systems.
• Architect scalable AI/ML platforms, services, and workflows with strong engineering best practices.
• Own the end‑to‑end DS lifecycle: problem definition, experimentation, deployment, and monitoring.
• Build reproducible, well‑documented model pipelines and production‑grade ML systems.
• Drive model QA/QC processes, including validation, drift detection, observability, and experiment tracking.
• Lead initiatives across AI, product, and engineering teams with strong execution and agile thinking.
• Collaborate closely with product, engineering, and domain teams to define modeling strategies and roadmap priorities.
• Mentor and guide team members on ML systems, architecture decisions, and engineering excellence.
• Continuously improve system reliability, scalability, documentation, and development processes.

Skills

Python

ML

NLP

LLMs

Pandas

NumPy

Scikit-learn

PyTorch

TensorFlow

MLflow

Experiment tracking

Model registries

APIs

Cloud

Containers

Stakeholder management

Tools

Jupyter

Git

Job description

Responsibilities:

• Design, architect, and scale AI/ML and LLM-powered systems that drive personalization, assist teachers, and improve learning programs.
• This role combines hands‑on data science expertise with strong engineering leadership and cross‑functional ownership.
• Lead the design, development, and deployment of ML, deep learning, and LLM-based systems.
• Architect scalable AI/ML platforms, services, and workflows with strong engineering best practices.
• Own the end‑to‑end DS lifecycle: problem definition, experimentation, deployment, and monitoring.
• Build reproducible, well‑documented model pipelines and production‑grade ML systems.
• Drive model QA/QC processes, including validation, drift detection, observability, and experiment tracking.
• Lead initiatives across AI, product, and engineering teams with strong execution and agile thinking.
• Collaborate closely with product, engineering, and domain teams to define modeling strategies and roadmap priorities.
• Mentor and guide team members on ML systems, architecture decisions, and engineering excellence.
• Continuously improve system reliability, scalability, documentation, and development processes.

Key Success Factors:

• Strong ownership mindset with the ability to independently lead initiatives and teams.
• High engineering standards, clean architecture thinking, and excellent documentation practices.
• Strong mission alignment and passion for building impactful AI‑driven products.
• Agile mindset with the ability to adapt, prioritize, and execute in fast‑moving environments.
• Curiosity, problem‑solving ability, and a bias toward shipping reliable, scalable systems.

Requirements:

• 3+ years of hands‑on experience building and deploying AI/ML systems in production.
• Strong experience in applied ML, NLP, LLMs, and modern AI workflows.
• Strong fundamentals in statistics, ML algorithms, deep learning, and system design.
• Proficiency in Python and ML ecosystem tools (pandas, numpy, scikit‑learn, PyTorch/TensorFlow).
• Experience with ML workflow tools such as MLflow, experiment tracking, and model registries.
• Hands‑on experience with LLMs, fine‑tuning, embeddings, vector databases, and RAG pipelines.
• Experience deploying models/services through APIs, cloud infrastructure, or containerized workflows.
• Strong collaboration and stakeholder management skills across product and engineering teams.