Machine Learning Engineering Lead for Software Factory Efficiency

11 hours ago

Singapore GASOIL PTE. LTD. Full time

We are seeking an experienced Machine Learning Engineering Lead to drive the development and implementation of AI-driven solutions that enhance software factory efficiency.

About the Role

The ideal candidate will have a strong background in MLOps, DevOps, and AI experimentation, with expertise in rapid prototyping and MVP development.

Key Responsibilities:
  • AI Use Case Development and Prototyping
    • Develop and rapidly iterate on AI-driven prototypes that support and streamline developer workflows, including code specification, code generation, and testing automation.
    • Collaborate with product and DevSecOps teams to identify high-impact AI use cases that improve software development and delivery efficiency.
    • Drive proof of concept (PoC) initiatives, transforming experimental AI ideas into feasible and scalable solutions.
    • Implement automation to improve repeatability and reduce manual tasks in the development pipeline, such as auto-code generation, static code analysis, and intelligent error detection.
    • Integrate automation tools that improve developer productivity, streamline testing, and optimize release cycles.
    • Stay current with the latest advancements in Al and machine learning technologies.
  • MVP Development and Iterative Testing
    • Build Minimum Viable Products (MVPs) for new AI solutions, focusing on quick deployment, testing, and user feedback.
    • Establish efficient testing and evaluation frameworks to assess the effectiveness of AI models and rapidly iterate on improvements.
    • Collaborate with developers and QA teams to integrate AI-based prototypes into the broader software lifecycle and measure productivity impact.
  • End-to-End ML Pipeline Development
    • Design and deploy scalable ML pipelines tailored to rapidly evolving prototypes, with robust model training, testing, deployment, and monitoring processes.
    • Manage versioning, model retraining, and performance tracking to ensure the continuity of high-quality AI solutions in the production environment.
    • Collaborate with cross-functional teams to iterate on solutions based on developer feedback and usage data.
    • Establish version control, deployment, and monitoring standards for ML models across the production environment.
    • Develop tools and processes for A/B testing, canary releases, and other ML model rollout techniques.
    • Ensure ML models are efficiently integrated within the internal Software Factory.
  • Collaboration with DevSecOps Team
    • Work closely with DevSecOps engineers to integrate ML workflows with existing CI/CD pipelines.
    • Enhance and support security measures for ML processes, ensuring compliance with DevSecOps policies and protocols.
    • Write scripts and automate workflows to manage ML pipeline processes, ensuring faster, reliable, and secure model deployments.
    • Integrate automation into DevSecOps workflows, ensuring repeatability and reducing manual intervention.
    Requirements:

To succeed in this role, you will need:

  • A Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (Master's degree preferred).
  • 3+ years of experience in MLOps, DevOps, or AI experimentation with a strong focus on rapid prototyping and MVP development.
  • Strong understanding of DevSecOps practices, tools, and methodologies.

This is an excellent opportunity to leverage your skills and expertise to drive innovation and efficiency in our software factory. The estimated salary for this position is around $120,000 - $150,000 per annum, depending on experience and qualifications.

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