Job Summary

The Role:

- Develop an understanding of Grab, Financial Services and Banking data and its nuances across countries. This understanding will help build predictive models for Financial Services product cross-sell, hyper-personalised messaging and channel optimization.
- Use LLMs to power the next generation of customer experiences.
- Manage the entire end-to-end lifecycle of ML models and AI agents, from development, deployment, optimization and maintenance.
- Work with engineering, compliance & operation teams to create solutions and product changes, informed by your findings and business inputs.
- Stay current with the latest AI/LLM research and new frameworks and incorporate them into practical applications.
- Work as an individual contributor or in a team to solve complex problem statements.
- Individual contributor role with 5+ years of experience expected.

Responsibilities

- Predictive Modelling: Build predictive models using machine learning to solve for classification, ranking and sequential learning. Engineer features from internal data assets to build refined transaction and customer profiles.
- MLOps & Automation: Build end-to-end MLOps pipelines to automate model training, deployment, and monitoring. Track production performance and implement feedback loops for continuous improvement.
- Generative AI Development: Design and deploy Agentic solutions using prompt engineering, agent orchestration, and LLM fine-tuning (e.g., LoRA, PEFT).



Build evaluation datasets and define guardrails for ongoing evaluation and monitoring.
- Ownership and Collaboration: Design AI solutions by working backwards from our needs. Lead the delivery of AI capabilities, from concept through production deployment

Qualifications

The Must Haves:

- Core ML: Expert proficiency in Python, Spark, and SQL (Presto/Hive) with a command of fundamental ML concepts (Bagging, Boosting, Online Learning, Recommendation Engines).
- Generative AI & Agent Frameworks: Hands-on experience with Agentic frameworks (specifically LangGraph, LangSmith, LangChain), LLM orchestration and evals
- MLOps & Productionization: Expertise in productionizing ML solutions with MLOps tools (MLflow, Kubeflow, TFX, SageMaker) and deploying scalable components on cloud platforms.
- Real-Time Streaming: Experience in the real-time implementation of machine learning solutions using Flink SQL and Flink SDK is a positive to have

Adherence to clean coding, modular design, and version control (Git/GitHub) characterises Engineering Excellence. Additionally, it involves the ability to balance model performance with business trade-offs, such as latency, cost, and scalability.

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 Scientist (Bengaluru)
🏢 Grab Greco
📍 Bengaluru