Machine Learning Engineering Lead

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This exclusion clause shall take effect to the fullest extent permitted by law.You further consent to Oversea-Chinese Banking Corporation Limited, its related corporations (collectively, the "OCBC Group"), and their respective business partners and agents (collectively, the “OCBC Representatives”) collecting, using and disclosing your personal data for purposes reasonably required by the OCBC Group and the OCBC Representatives to enable them to process your employment application and assess your suitability for the position which you are applying for. Such purposes are set out in a Data Protection Policy, which is accessible at or available on request and which you confirm you have read and understood.Machine Learning Engineering Lead - VP page is loaded## Machine Learning Engineering Lead - VPremote type: Onsitelocations: OCBC Singaporetime type: Full timeposted on: Posted Todayjob requisition id: JR # **WHO WE ARE:** Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future. We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career.Your Opportunity Starts Here.# We are looking for a highly experienced and technically proficient MLOps Engineering Lead to spearhead the strategy, design, and implementation of our MLOps platform, with a critical focus on operationalizing and managing our Generative AI (GenAI) Compute Cluster.**Why Join**Imagine being part of a team that harnesses the power of GenAI and machine learning to drive business growth and innovation at OCBC. As a MLOps Engineering Lead, you'll play a key role in developing and deploying AI solutions that transform the way we deliver financial services. You'll have the opportunity to drive innovation and transformation in the banking industry, work on complex problems, collaborate with cross-functional teams, and see the impact of your work on our customers and business.**How you succeed**To succeed in this role, you'll need to stay at the forefront of machine learning advancements and apply your expertise to drive business outcomes. You’re responsible for architecting, building, and operating a production‑grade MLOps platform that powers AI‑driven products in the company.This role demands a blend of technical leadership, deep expertise in MLOps best practices, and hands-on experience in managing large-scale, accelerated computing infrastructure (e.g., GPU clusters) essential for training and serving large language models (LLMs) and other GenAI models. Ensure the reliability, scalability, performance, and cost-efficiency of our entire ML lifecycle, from data ingestion to model deployment and monitoring. You'll also lead a team of machine learning engineers, providing guidance, mentorship, and support to help them grow and develop in their careers. Your technical expertise will be critical in designing and implementing scalable, efficient, and reliable machine learning systems.**What you do**MLOps and Platform Leadership* Serves as the primary owner and technical custodian of Enterprise Data Science Platform, ensuring seamless integration with MLOps tools, data and security frameworks to support data science activities.* Define the technical roadmap and architecture for our end-to-end MLOps platform, incorporating CI/CD, feature stores, model registries, and artifact management* Design and implement robust, automated CI/CD pipelines for continuous training, integration and delivery of both traditional and GenAI models.* Implement comprehensive monitoring (e.g., Prometheus, Grafana) for model performance, resource utilization, latency, and system health in production environments.GenAI Cluster Operationalization* Take ownership of the GenAI compute cluster to ensure maximum uptime, optimal performance, and efficient resource allocation.* Implement and manage high performance LLM serving solution (e.g. VLLM) to achieve high throughput, low latency, efficient memory utilization for production GenAI APIs.* Implement and manage advanced job scheduling, resource queueing, and quota systems (e.g. Ray) tailored for large-scale, multi-node distributed model training.* Continuously benchmark and optimize the cluster and software stack for GenAI workloads, focusing on compute resource utilization and performance.People & Project Management* Mentor a team of machine learning engineers, providing guidance on career growth, technical problem solving, and continuous learning.* Work closely with data scientists, data engineering, and software teams to deliver high-performance production systems.* Collaborate with product management and stakeholders to define project scopes, align on timeline for project delivery* Guide architectural decisions and resolve technical impediments in a timely manner**Who you are*** Experience with one or more major cloud providers (AWS, GCP, Azure) and their core ML services (e.g., SageMaker, Vertex AI, Azure ML).* Hands-on experience with the specific infrastructure, tools, and operational challenges of training and deploying LLM and classical ML models.* Familiar with MLOps best practices, including CI/CD pipelines, model versioning, feature stores, and ML monitoring/observability tools.* Proficient in programming languages commonly used in ML (e.g. Python)* Experience implementing pipelines using tools like Bitbucket, Jenkins, or Dagster.* Familiar with vector databases and MLOps practices specific to Retrieval-Augmented Generation (RAG) systems.* Experience leading teams of engineers, with a focus on mentorship, guidance, and career development* Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams* 5+ years of experience in MLOps or ML Engineering, with a strong track record of delivering high-quality solutions.* Bachelor's or Master’s degree in Computer Science, AI and Machine Learning, or a related technical field.**Who we are** Today, we're on a journey#J-18808-Ljbffr