Sr Associate AI & Data Science

Key Responsibilities

• Hands-On Execution (The "What") - Model Development: Build, train, and evaluate ML and GenAI models, taking them from experimentation to production with guidance from senior engineers.
• Pipelines: Implement and maintain data and inference pipelines, ensuring they meet reliability and latency targets.
• Collaboration: Work closely with senior architects and engineers, contributing to code reviews and learning production best practices.

The Ideal Candidate Profile

You are a hands-on builder.

• You love to code, you are curious about how things work end-to-end, and you are eager to grow into a scale-veteran engineer.
• Essential Experience (3+ Years) - Dual Exposure: Hands-on experience in Classic ML AND/OR Modern GenAI (LLMs/Agents).
• Bonus if you have shipped either type of system to production.
• Scale Curiosity: Experience working with production systems and a strong desire to understand high-throughput, distributed computing.
• Deep Learning Fundamentals: Solid grasp of ML/DL fundamentals and the underlying math.
• Familiarity with Transformers or GNNs is a plus.
• Collaboration: Comfortable working in cross-functional teams and aligning your work with business goals.

Must-Have Skills

• Programming: Strong proficiency in Python and solid software engineering fundamentals (clean code, version control with Git, testing).
• Core ML: Hands-on experience building, training, and evaluating ML models using scikit-learn, XGBoost/LightGBM, and PyTorch or TensorFlow.
• GenAI / LLMs: Practical experience with LLMs - prompt engineering, RAG, fine-tuning, and frameworks such as LangChain/LangGraph and Hugging Face Transformers.
• Data Handling: Strong SQL and data wrangling skills (Pandas/NumPy); experience working with large datasets.
• Big Data / Pipelines: Working experience with Spark or Kafka/Flink for building data and inference pipelines.
• Deployment: Experience containerizing and deploying models (Docker), exposing them via REST/gRPC APIs, and basic familiarity with Kubernetes.
• MLOps: Exposure to experiment tracking and model lifecycle tools (MLflow) and CI/CD for ML.
• Cloud: Hands-on with at least one major cloud platform (AWS, GCP, or Azure).
• Fundamentals: Solid grasp of statistics, probability, and deep learning concepts (including Transformer architecture).
• Communication: Ability to clearly document work and collaborate within cross-functional teams.

Technical Stack

• ML Ops: Kubernetes, Kubeflow, Ray, MLflow.
• Big Data: Spark, Kafka, Flink, Iceberg/Hudi.
• GenAI: vLLM, LangGraph, DeepSpeed, Unsloth.
• Cloud/Infra: Working knowledge of GPU/TPU usage and cost awareness.

Growth & Resources

Growth: Learn directly from Principal Architects and senior engineers shaping the nations AI.

Resources: Access to state-of-the-art compute infrastructure and proprietary datasets that are unique in the world.

Legacy: The opportunity to say, "I helped build the AI that powers the Indian Economy."

Job Details

• Job Type: Full Time
• Job Opening Name: Senior Associate AI & Data Science
• Location: Maharashtra

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.