Data Scientist AIML

Location- MUM/BLR/CHN/HYD/GUR

Role & responsibilities

• Explore, clean, and analyse large, complex datasets to uncover patterns, trends, and opportunities that drive actionable insights.
• Develop, train, and validate machine learning, statistical, and predictive models that solve real business problems and deliver measurable impact.
• Design and run experiments (A/B tests, hypothesis tests, simulations) to evaluate ideas, quantify outcomes, and guide decision-making.
• Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into well-defined modelling tasks.
• Build end-to-end ML pipelinesfrom feature engineering and preprocessing to deployment-ready model outputs.
• Apply advanced techniques such as NLP, time-series forecasting, anomaly detection, optimisation, or LLM/GenAI methods where relevant.
• Build and ship production-ready AI/ML featuresfrom data ingestion and feature engineering to model training, evaluation, and deployment.
• Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re-ranking).
• Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/TPU), and efficient memory/latency tuning.
• Build and maintain MLOps/LLMOps workflowsCI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments.

Preferred candidate profile

• Strong hands-on experience building and deploying machine learning models, including preprocessing, feature engineering, training, evaluation, and optimisation.
• Knowledge of API Gateways and ISTIO , ability to Diagnose and intercept failures in End to End communication.
• Implement best practices for data governance, security, and MLOps on GCP.
• Proficiency with Python and common AI/ML frameworks such as TensorFlow, PyTorch, JAX, scikit-learn, and Hugging Face libraries.
• Knowledge of MLOps and LLMOps practicesincluding CI/CD for models, model registry/versioning, feature stores, orchestration, and automated deployments.
• Strong experience applying machine learning, statistical modelling, and predictive analytics to real-world business problems.
• Collaborate with cross-functional teams to ability to resolve end to end connectivity and Data Integrations
• Experience working with large, complex datasets, including data cleaning, feature engineering, and exploratory data analysis.
• Familiarity with LLMs, NLP techniques, and GenAI frameworks, including embeddings, prompt engineering, or fine-tuning.
• Experience building end-to-end ML pipelines, including model validation, optimisation, deployment, and monitoring.