Assistant Manager, AIOps Engineering
FWD Group · Malaysia · 1mo ago
About FWD Group
FWD Group (1828.HK) is a pan-Asian life and health insurance business that serves approximately 40 million customers across 10 markets, including BRI Life in Indonesia. FWD’s customer-led and tech-enabled approach aims to deliver innovative propositions, easy-to-understand products and a simpler insurance experience. Established in 2013, the company operates in some of the fastest-growing insurance markets in the world with a vision of changing the way people feel about insurance. FWD Group is listed on the main board of the Hong Kong Stock Exchange under the stock code 1828.
For more information, please visit www.fwd.com
FWD Technology and Innovation Malaysia Sdn. Bhd., known as FWD TIM, was established in late 2019. Strategically located in Kuala Lumpur, FWD TIM serves as a pivotal shared service location within FWD Group, providing services to multiple markets across the Group. FWD TIM houses a diverse and talented workforce focused on essential business and technology services such as information security, cloud operations, IT solutions delivery, digital and data, actuarial, finance, investments, and customer service, among many others. FWD TIM is dedicated to drive and deliver operational excellence and efficiency, foster innovation and ensure regulatory compliance across all business functions as well as maintain a competitive edge in the market.
PURPOSE
To accelerate the delivery of strategic business outcomes, including product simplification, customer experience enhancement, multi-channel distribution, hyper-personalized interactions, and organizational agility, the AI Ops Engineer will work closely with the Group Data team to deliver enterprise data and AI solutions while supporting local entities in implementing their own data and AI initiatives.
This role ensures that both Group and local data/AI solutions are aligned with the organization's digital technology and infrastructure strategies, driving cloud standardization, optimization, and scalable AI adoption.
The AI Ops Engineer will collaborate extensively with the Group Data team to design, build, deploy, and operate AI/ML and Generative AI solutions, while continuously enhancing data and AI capabilities on the Group Data Platform (GODP). The role is also responsible for ensuring that business, application, data, and technology solution designs are aligned with the Group Data Strategy and enterprise architecture standards.
KEY ACCOUNTABILITIES
Collaborate with Data Architects, Data Scientists, Data Engineers, and Platform teams to design and deliver scalable data and AI solutions aligned with business objectives and analytical requirements.
Partner with business stakeholders to design, develop, and implement Generative AI and Agentic AI applications that drive business value and operational efficiency.
Work closely with Data Science teams to operationalize AI/ML models and integrate them into production environments.
Design, implement, and maintain MLOps pipelines to automate model training, validation, deployment, monitoring, and lifecycle management.
Support the development, optimization, scaling, and reliability of AI/ML and Generative AI solutions to ensure production readiness and operational excellence.
Ensure compliance with enterprise data governance, security, privacy, and regulatory requirements.
Drive continuous improvement initiatives through automation, performance optimization, platform modernization, and adoption of emerging AI technologies.
Provide production support for AI applications, AI models, and data pipelines, including incident investigation, root cause analysis, troubleshooting, and resolution.
Maintain comprehensive technical documentation, operational procedures, and knowledge repositories.
Mentor and guide team members, promoting engineering best practices, innovation, collaboration, and continuous learning.
KEY PERFORMANCE INDICATORS
Achievement of individual and team performance objectives.
Successful delivery of projects within agreed scope, budget, quality, and timeline.
Availability, reliability, and performance of AI and data solutions in production.
Customer and stakeholder satisfaction.
Adoption and business impact of delivered AI solutions.
Continuous improvement through automation and operational efficiency gains.
EXTERNAL & INTERNAL CONTACTS
Internal
Report to AIOps team leader
Communication with team members
Actively work together with data product, data scientist team, data engineer team and data platform team.
External
Digital and data delivery vendors
DECISION MAKING
In accordance with delegated authority
QUALIFICATIONS / EXPERIENCE
Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, or a related discipline.
Minimum 5 years of relevant experience in Data Engineering, MLOps, AI Engineering, or related fields.
Strong analytical thinking, problem-solving, and troubleshooting capabilities.
Proficient in Python, Spark, and software engineering best practices.
Hands-on experience developing and deploying Generative AI, LLM-based, and Agentic AI applications.
Familiarity with metadata management.
Familiarity with AI-assisted development and Vibe Coding tools such as GitHub Copilot, OpenAI Codex, and similar technologies.
Strong understanding of machine learning concepts, model development lifecycle, and model deployment practices.
Experience with cloud platforms such as Microsoft Azure and/or AWS.
Hands-on experience with Databricks and modern data platforms.
Experience with MLOps and DevOps tools, CI/CD pipelines, model monitoring, and infrastructure automation.
KNOWLEDGE & TECHNICAL SKILLS
Knowledge of modern AI frameworks and ecosystems, including LLM orchestration, prompt engineering, vector databases, and agent frameworks, is highly desirable.
Experience in web application development would be an advantage.
Experience in the insurance or financial services industry would be an advantage.
Strong communication and presentation skills, with the ability to explain technical concepts to both technical and non-technical audiences.
Ability to work independently while collaborating effectively across multiple teams and stakeholders.
Strong ownership mindset, accountability, and commitment to delivering high-quality solutions.
Passion for data, AI, innovation, and continuous improvement.