Senior AI Engineer

Responsibilities

The AI Engineer will design and deploy enterprise-grade agentic solutions and transformer-based models using large-scale transactional data. They will manage the complete lifecycle of AI systems from experimentation through to production deployment, monitoring, and maintenance.

Requirements

Candidates must have 5+ years of experience and hands-on expertise in deploying enterprise-grade agentic solutions and transformer architectures. Proficiency in Python, Spark, SQL, and AI productionization tools is required.

Skills

• Python
• Spark
• SQL
• Generative AI
• Large Language Models
• Transformer architectures
• Agentic workflows
• Machine learning
• Data science
• MLflow
• Kubeflow
• LangChain
• LangGraph
• Retrieval-augmented generation
• Cloud infrastructure
• System design

Benefits

• Term Life Insurance
• Medical Insurance
• GrabFlex benefits package
• Parental leave
• Birthday leave
• Volunteering leave
• Grabber Assistance Programme

About this role

Company Description
About Grab and Our Workplace
Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.
Job Description
Get to know the team
Grab's Financial Services business is present across 6 countries in South East Asia, in the Payments, Lending, Insurance and Digital Banking domain. We station our engineering teams in India, Singapore, Indonesia, Malaysia and Vietnam. We are excited to provide financial services to all participants of the ecosystem be it our Consumers, Drivers or Merchants. We build our products on fundamental market insights combined with advanced Data Science, Generative AI and engineering to bring the best product market fit across the cross section of our user base. This understanding of our ecosystem combined with world class engineering execution continues to create tremendous value for our customers.
We base the Financial Services Data Science and AI team across Bangalore, Kuala Lumpur, and Singapore. We are looking for an AI Engineer to join and expand our Bangalore team. The AI Engineer will work in a relatively flat team structure with an independent goal of developing critical AI solutions. You will focus on developing enterprise-grade agentic applications, fine-tuning large language models and building transformer-based foundational and promoting models using Grab's rich transactional data. You are passionate about AI and engineering, has and has taken complex AI systems from concept to production. Grab bases this role in its Bangalore office.
The Role:
Develop an understanding of Grab's Financial Services and Banking data and its nuances across countries.
Deploy enterprise-grade agentic solutions that power the next generation of customer experiences.
Develop AI solutions for use cases such as representation learning, conversational AI and agentic operations.
Fine-tune and adapt LLMs using techniques such as supervised fine-tuning, instruction tuning, LoRa and PEFT.
Build transformer-based models from scratch, including foundational models and promoting models trained on large-scale transactional data.
Manage the complete lifecycle of AI systems from experimentation and evaluation through deployment, optimization, monitoring, and maintenance.
Work with engineering, product, compliance, risk, and operations teams to build reliable and responsible AI solutions.
Stay current with AI and LLM research, frameworks, and new techniques, and translate them into practical applications.
Or collaboratively to solve complex and ambiguous technical problems.
Operate as an individual contributor with 5+ years of relevant experience.
The daily activities:
Enterprise Agentic AI: Design and deploy production-grade agentic systems using LLM orchestration, tool use, planning, memory, retrieval, and multi-agent workflows. Build for reliability, observability, security, scalability, and human oversight.
LLM Development and Fine-Tuning: Fine-tune and evaluate LLMs using techniques such as supervised fine-tuning, instruction tuning, LoRA, and PEFT. Build high-quality training and evaluation datasets and increase models for accuracy, latency and cost.
Transformer and Foundation Model Development: Design and implement transformer architectures from scratch. Train foundational or domain-specific representation and promoting models using large-scale transactional and behavioural data.
Evaluation and Guardrails: Define offline and online evaluation frameworks for AI agents and models. Develop evaluation datasets, safety guardrails, monitoring systems and feedback loops to improve production performance.
Ownership and Collaboration: Design AI solutions by working backwards from business and customer needs. Lead delivery from problem definition and architecture through production deployment and ongoing operation.
Qualifications
The Must Haves:
Experience designing and deploying at least one enterprise-grade agentic solution in production, with practical experience in tool integration, orchestration, state and memory management, evaluation, guardrails, observability and failure handling.
I have hands-on experience with agentic frameworks such as LangGraph, LangSmith, and LangChain. Additionally, I have a understanding of retrieval-augmented generation, LLM orchestration, tool calling, and agent evaluation.
Transformer Architecture: understanding of transformer architectures, including attention mechanisms, tokenization, positional encoding, training goals, optimization, and inference. You can implement and train a transformer-based model from scratch
Core ML: Expert proficiency in Python, Spark, and SQL (Presto/Hive) with a command of fundamental ML concepts (Bagging, Boosting, Recommendation Engines)
AI Productionization: Expertise in deploying scalable AI and ML systems using tools such as MLflow, Kubeflow, TFX, or SageMaker, together with experience in model serving, monitoring, automated evaluation and cloud infrastructure.
Adherence to clean coding, modular system design, testing, APIs, and version control (Git/GitHub). Balance quality with production considerations such as latency, cost, reliability, security, and scalability.
Additional Information
Life at Grab
We care about your well-being at Grab, here are some of the global benefits we offer:
We have your back with Term Life Insurance and comprehensive Medical Insurance.
With GrabFlex, create a benefits package that suits your needs and aspirations.
Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
We have a confidential...

About Grab

Software Development · 5,001-10,000 employees · headquartered in Singapore, Singapore · founded 2012