Senior Data Engineer (Data Intelligence)

Job Summary

Develop and optimize production-grade predictive ML models and generative AI capabilities, constructing MLOps training pipelines and advanced LLM interaction logic while providing technical guidance to other engineers.

Responsibilities

• Develop and optimize predictive ML models for use cases such as demand forecasting and inventory optimization, and the Layla advisor.
• Construct and maintain MLOps training pipelines across cloud AI platforms (e.g. Azure AI, Google Vertex AI, AWS SageMaker), including experiment tracking, model versioning, and reproducible training.
• Write and optimize advanced LLM prompt interaction logic for the companys enterprise GenAI platform (Alia), including retrieval-augmented generation and vector database integration.
• Build and implement generative AI and agentic solutions using LLM APIs and orchestration frameworks and help evaluate and adopt new AI tools within the squad.
• Engineer features and build feature stores using SQL and Python , working with structured, semi-structured, and time-series data to improve model performance, precision, and recall. Data and AI Reports to Senior Manager, Data Intelligence Bengaluru.
• Deploy and integrate model inference endpoints, both real-time and batch, via REST and GraphQL APIs into production systems.
• Establish model performance metrics (accuracy, F1, AUC) and LLM evaluation metrics (e.g. relevance, groundedness, hallucination rate), and monitor for model drift, defining retraining strategies and triggers.
• Apply fairness and bias-mitigation techniques and support responsible AI and model governance practices.
• Link model outputs to business KPIs and value creation, and document models, experiments, and assumptions for reproducibility and reuse.
• Provide technical guidance and mentoring to AI/ML engineers, and collaborate with data engineering, MLOps, and product teams to integrate models into production.

What you'll need to succeed

• Minimum 5 years of experience in applied machine learning or AI engineering.
• Strong proficiency in Python and machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), with feature engineering and experiment tracking (MLflow).
• Strong SQL skills for data exploration, feature engineering, and working with cloud data warehouses (e.g. BigQuery, Snowflake).
• Hands-on experience constructing MLOps training pipelines across Azure AI and GCP, with model versioning and monitoring.
• Experience with generative AI and large language models, including prompt engineering, retrieval-augmented generation, and vector databases.
• Hands-on experience implementing AI solutions with LLM APIs and models (e.g. OpenAI, Anthropic Claude, Google Gemini, open-source models), orchestration frameworks (e.g. LangChain, LangGraph, LlamaIndex), and tools for evaluation, guardrails, and LLM observability (e.g. LangSmith, Langfuse).
• Solid grasp of the model development lifecycle: training, hyperparameter tuning, evaluation, deployment, drift detection, and retraining.
• Experience deploying model inference endpoints via REST and GraphQL APIs, with fairness, bias mitigation, and responsible AI practices.
• Hands-on experience with AI coding assistants (e.g. Claude Code, GitHub Copilot, Cursor) for AI-assisted development.

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