Senior AI MLOps - Global Financial Services

A leading global financial services firm is seeking a Senior AI MLOps to join their AI enablement team and an opportunity to design, develop, and operate cutting-edge AI/ML and Gen AI platforms that drive high-impact use cases across Asia.

Key responsibilities:

As a Senior Machine Learning Operations Engineer, you will play a critical role in advancing the organisation's AI capabilities by building scalable systems and collaborating with diverse teams.

AI/ML Engineering & Data Pipelines

• Design, develop, and maintain scalable batch and streaming data pipelines to deliver high-quality curated datasets for ML and GenAI initiatives
• Collaborate with Data Scientists and IT teams to validate data quality, define reliable data contracts, and optimise feature engineering, data modelling, and dataset versioning for reproducibility
• Support AI-driven strategic initiatives by ensuring robust, efficient, and scalable data infrastructure and operations

Model Development, Deployment & Operations (MLOps/LLMOps)

• Manage the full ML/LLM lifecycle, including model training, evaluation, optimisation, deployment, monitoring, and continuous improvement
• Develop and maintain production-grade CI/CD pipelines and scalable AI infrastructure using Azure, Databricks, and Kubernetes-based environments
• Establish model governance and operational monitoring for performance, data drift, hallucination risks, safety controls, and reusable AI platform components to accelerate development across teams

Collaboration & Solution Delivery

• Collaborate with product owners, data scientists, and business stakeholders to translate business requirements into scalable AI and GenAI solutions
• Provide technical leadership on data architecture, modelling strategies, GenAI capabilities, and AI operational best practices
• Develop and standardise reusable frameworks, templates, and development patterns to improve efficiency and ensure consistency across AI initiatives

Candidate profile:

To excel in this role, you will bring extensive experience in building production-grade machine learning systems within enterprise environments alongside strong technical expertise in MLOps practices.

• Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related technical field
• 5–8 years of experience building and deploying production-grade ML systems, data pipelines, and AI platform solutions
• Strong expertise in Python, SQL, PySpark, and cloud-native technologies including Azure, Databricks, Spark, and Delta Lake
• Hands-on experience with MLOps and LLMOps, including CI/CD, model deployment, monitoring, drift detection, model registry, and prompt management
• Practical knowledge of LLM and GenAI frameworks such as Transformers, LangChain, Azure OpenAI, vector databases, and retrieval systems, with strong understanding of ML algorithms, distributed systems, and optimisation techniques
• Proven ability to deliver business-driven AI solutions with strong communication skills; experience in customer analytics, penalisation, or conversational AI is an advantage

About the company:

A leading company in the financial services and insurance industry, providing a broad range of wealth management. The organisation is recognised for its strong regional presence, customer-focused approach, and continued investment in digital innovation and data-driven technologies to enhance client experience and operational efficiency.

Keywords: AI/ML engineering, data pipelines, MLOps, LLMOps, cloud-native environments, GenAI platforms

What's next?

If you're ready to elevate your career in machine learning operations within an influential global setting, apply now!