Staff Machine Learning Engineer

BP p.l.c.; Malaysia; On-site; MYR 60,000 - 120,000; Full time

Job summary

An established industry player is seeking a Staff MLOps Engineer to spearhead the deployment of machine learning models. This pivotal role involves defining MLOps strategies, mentoring teams, and ensuring seamless integration of ML systems into business-critical applications. You will be responsible for optimizing infrastructure, implementing security protocols, and enhancing automation within production environments. Join a dynamic team dedicated to driving digital transformation in the energy sector, where your expertise will contribute to operational efficiency and innovation. If you are passionate about AI and machine learning and want to make a significant impact, this opportunity is for you!

Benefits

Generous salary package

Annual bonus program

Flexible working arrangements

Ongoing career development

16 weeks paid parental leave

Qualifications

• 5+ years in software development or MLOps, leading ML models in production.
• Strong understanding of MLOps principles, CI/CD, and infrastructure automation.

Responsibilities

• Define architectural design of automated CI/CD pipelines for AI goals.
• Lead and mentor teams in MLOps practices, ensuring operational excellence.

Skills

MLOps Principles

Machine Learning Operations

CI/CD Pipelines

Cloud Platforms (AWS, Azure)

Model Monitoring

Technical Leadership

Education

Bachelor's in Computer Science

Master's in Data Science

Tools

Prometheus

Grafana

Datadog

Job description

Job Summary

Entity: Technology

Job Family Group: IT&S Group

We are looking for a Staff Machine Learning Operations (MLOps) Engineer to lead our efforts in delivering robust, scalable, and secure deployment of machine learning models at bp. This role is pivotal in defining MLOps strategies, mentoring teams, and driving alignment across data science, software engineering, and operational teams to accelerate bp’s digital transformation. The Staff MLOps Engineer will ensure that machine learning models integrate seamlessly into business-critical systems, enabling impactful decision-making and innovation in the energy sector!

The MLOps Engineer will be responsible for deploying, scaling, and maintaining machine learning models in production environments, building automated CI/CD pipelines, and optimizing infrastructure for performance and reliability. By leveraging modern MLOps practices and collaborating closely with data scientists, software engineers, and DevOps teams, this role will contribute to enhancing automation, improving system resilience, and ensuring high standards of security and compliance across bp’s AI applications.

This position plays a crucial part in bp’s strategic goal to harness AI and machine learning for operational efficiency, informed decision-making, and innovation. The MLOps Engineer’s contributions are vital to ensuring that bp remains competitive and agile in the evolving energy landscape, while upholding the highest standards of security, performance, and reliability in our AI systems.

Key Accountabilities

• Define and oversee the architectural design of automated CI/CD pipelines, ensuring they align with bp's long-term AI goals and industry best practices.
• Act as a technical leader and advisor, aligning cross-functional teams to strategic goals, ensuring perfect integration of machine learning systems at scale.
• Supervise and optimize machine learning infrastructure and model performance to detect issues proactively and ensure continuous operational efficiency and system health in production environments.
• Implement security best practices and compliance protocols for machine learning systems to safeguard bp’s data, ensure model integrity, and comply with data protection regulations.
• Lead and mentor senior engineers, fostering a culture of operational excellence and continuous learning.

Essential Experience and Job Requirements

• Membership of a technical or professional body that has confirmed an individual has reached a level of capability within a technical or professional field (usually but not necessarily obtained through examinations) or Formal certification that they can undertake specialist work or processes e.g. PMI.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 5+ years experience in professional software development or machine learning operations, with a proven track record of deploying and leading ML models in production environments.
• Strong understanding of MLOps principles and practices, including model versioning, monitoring, CI/CD, and infrastructure automation.
• Extensive experience leading large-scale MLOps initiatives, including designing and handling production systems with high scalability and reliability requirements.
• Experience with cloud platforms (e.g., AWS, Azure) and managing machine learning workloads within these environments, including understanding of cloud-native tools and best practices.
• Proven track record of driving consensus and influencing stakeholders at all levels to adopt cutting-edge MLOps strategies.
• Demonstrated ability to lead or mentor junior engineers in MLOps practices, contributing to a culture of knowledge-sharing and continuous learning within the team.
• Experience with sophisticated model monitoring and observability tools (e.g., Prometheus, Grafana, Datadog) to supervise model performance and system health in real time.

What we offer:

It’s crucial to us that the differences we see in the world around us are reflected in our workplace. Who you are is what counts, not where you’re from or how you live your life. At bp, we support our people to learn and grow in a diverse and challenging environment. We believe that our team is strengthened by diversity. We are committed to fostering an inclusive environment in which everyone is respected and treated fairly. There are many aspects of our employees’ lives that are meaningful, so we offer benefits to enable your work to fit with your life. These benefits include:

• Generous salary package including an annual bonus program and individual performance-based incentives.
• Additional EPF contributions totaling 15%.
• Excellent work-life balance & flexible working arrangements!
• Collaborative environment that celebrates achievements, diversity, and culture
• Ongoing career development and progression opportunities in a global organization
• 16 weeks paid parental leave (4 weeks partner leave)