Employer description & source details
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. 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
Extracted skills: python, sql, security, operations