AI Engineering (Assistant Manager) – AIOps

HFG Insurance Recruitment; Kuala Lumpur; On-site; MYR 120,000 - 240,000; Full time

Job summary

HFG Insurance Recruitment is seeking an AI Engineering / AIOps professional to support the delivery and operationalisation of strategic AI, ML and data solutions across the organisation. You will collaborate with Data Scientists, Data Engineers, Data Architects and Data Platform teams to design, deploy and operate scalable AI/ML solutions.

This role focuses on MLOps, GenAI, production support, automation and continuous improvement, with potential to progress into a technical leadership role.

Qualifications

• Minimum 5 years of experience in MLOps, AI Engineering, or related fields.
• Hands-on with MLOps, Generative AI and end-to-end AI/ML lifecycle.
• Proficiency in Python and Spark for production-grade solutions.
• Experience with Databricks and modern data platforms.
• Experience with Azure and/or AWS cloud environments.

Responsibilities

• Design, develop, deploy and operationalise AI/ML and GenAI solutions.
• Manage end-to-end AI/ML lifecycle including monitoring and optimisation.
• Build and maintain MLOps/DevOps pipelines (CI/CD, deployment, monitoring).
• Collaborate with Data Scientists/Engineers/Architects to translate requirements into scalable solutions.
• Contribute to cloud standardisation and AI/data workload optimisation.
• Mentor engineers and lead AI/MLOps workstreams.

Skills

MLOps

AI Engineering

Data Engineering

Python

Spark

CI/CD

Databricks

Azure/AWS

GenAI

LLM-based AI

Tools

Databricks

CI/CD tools

Azure

AWS

GitHub Copilot

Job description

Business Unit: Technology & Innovation Management (TIM)

Department: Group Digital

Role Overview

We are looking for an AI Engineering / AIOps professional to support the delivery and operationalisation of strategic AI, machine learning and data solutions across the organisation.

The role will work closely with Data Scientists, Data Engineers, Data Architects and Data Platform teams to design, develop, deploy and operate scalable AI/ML and Generative AI solutions. The successful candidate will also support local business entities in implementing AI initiatives while ensuring alignment with enterprise technology, cloud, data and architecture strategies.

This position has a strong focus on MLOps, AI Engineering, GenAI, production support, automation and continuous improvement, with potential to progress into a technical leadership role.

Key Responsibilities

• Design, develop, deploy and operationalise AI/ML, Generative AI, LLM-based and Agentic AI solutions across enterprise environments.
• Support the end-to-end AI/ML lifecycle, including development, testing, deployment, monitoring, production support and continuous optimisation.
• Build and maintain scalable MLOps/DevOps pipelines, including CI/CD, automation, model deployment and monitoring.
• Work with modern data platforms such as Databricks to develop and operationalise AI and data solutions.
• Develop robust engineering solutions using Python, Spark and software engineering best practices.
• Collaborate with Data Scientists, Data Engineers, Data Architects and Platform teams to translate business requirements into scalable technical solutions.
• Contribute to cloud standardisation and optimisation across AI and data workloads, particularly within Microsoft Azure and/or AWS environments.
• Support the implementation of LLM orchestration, prompt engineering, vector databases and AI agent frameworks.
• Ensure AI and data solutions align with enterprise architecture, technology strategy and data governance standards.
• Troubleshoot production issues and continuously improve the availability, reliability, performance and operational efficiency of AI solutions.
• Contribute to technical discussions, solution design and architecture decisions.
• Potentially mentor engineers and take ownership of selected AI/MLOps projects or technical workstreams.

Key Requirements

Must Have:

• Minimum 5 years of relevant experience in MLOps, AI Engineering, Data Engineering or related fields.
• Strong hands-on experience in MLOps and Generative AI / AI Engineering.
• Strong understanding of the end-to-end AI/ML lifecycle, including development, deployment, production support and operationalisation.
• Strong hands-on experience with Python and Spark.
• Experience developing and deploying Generative AI, LLM-based and/or Agentic AI applications.
• Experience with MLOps/DevOps tools, CI/CD pipelines, model monitoring and infrastructure automation.
• Hands-on experience with Databricks and modern data platforms.
• Experience with Microsoft Azure and/or AWS.
• Ability to contribute to solution design and technical decision-making, with sufficient technical depth beyond pure implementation.

Good to Have:

• Knowledge of LLM orchestration, prompt engineering, vector databases and agent frameworks.
• Familiarity with metadata management.
• Exposure to GitHub Copilot, OpenAI Codex or similar AI-assisted development tools.
• Exposure to the insurance or financial services industry.

Key Performance Areas

• Successful delivery of AI/data projects within agreed scope, budget, quality and timelines.
• Availability, reliability and performance of AI/ML solutions in production.
• Successful adoption and business impact of AI solutions.
• Stakeholder satisfaction and effective collaboration across technology and data teams.
• Continuous improvement through automation, operational efficiency and platform optimisation.
• Achievement of individual and team objectives.

Stakeholder Collaboration

The role will work closely with:

• AIOps Engineering team
• Data Scientists
• Data Architects
• Data Product teams
• Data Platform teams
• Technology and Digital stakeholders
• Local business/entity technology teams

This role provides an opportunity to take on broader technical leadership responsibilities, including mentoring engineers, leading technical discussions, owning projects/workstreams and contributing to the organisation's wider AI, GenAI and MLOps strategy.

Given the limited availability of experienced Assistant Manager-level AIOps talent, candidates who do not meet every requirement may still be considered if they demonstrate a strong foundation in MLOps/AI Engineering, hands‑on technical capability and the potential to grow into broader technical and leadership responsibilities.