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.
- Robust 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.

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.

📌 Senior Data Engineer (Data Intelligence) (Bengaluru)
🏢 Chalhoub Group
📍 Bengaluru