Senior Machine Learning Engineer

2 weeks ago

Sydney, New South Wales, Australia iVEGA Consulting Full time $120,000 - $180,000 per year

Job type : Permanent (Full-time) / Contract both available

Job description :

iVEGA is seeking a highly skilled
Machine Learning Platform Engineer
to join our technology team supporting
a Leading Australian Bank
. You will be instrumental in developing, deploying, and managing end-to-end machine learning workflows in a scalable cloud environment. This role is ideal for professionals who are passionate about ML operations (MLOps), cloud infrastructure, and delivering secure and reliable AI/ML solutions at scale.

Key Responsibilities

  • Design, develop, and manage ML workflows
    using
    Amazon SageMaker
    ,
    AWS Glue
    , and
    Apache Airflow
    .
  • Deploy and monitor
    scalable applications and ML models on
    Amazon EC2
    and
    AWS Lambda
    .
  • Automate infrastructure provisioning
    and resource management using
    AWS CloudFormation
    .
  • Containerize applications
    using
    Docker
    and build robust
    CI/CD pipelines
    with
    GitHub Actions
    .
  • Collaborate with data scientists, software engineers, and product teams to
    integrate ML models into production environments
    seamlessly.
  • Write
    clean, efficient, and reusable Python code
    for automation, data processing, and orchestration.
  • Ensure
    security, scalability, and reliability
    of all ML systems and cloud-based deployments in compliance with banking standards.
  • Implement monitoring, logging, and alerting to ensure system health and performance.
  • Drive best practices in MLOps, including version control, testing, and reproducibility of ML experiments.

Required Skills and Experience

  • 5–7+ years
    of experience in MLOps, Cloud Engineering, or Machine Learning Engineering.
  • Strong hands-on experience with
    AWS services
    , particularly
    SageMaker
    ,
    Glue
    ,
    EC2
    ,
    Lambda
    , and
    CloudFormation
    .
  • Proficiency in
    Python
    for automation, ML workflows, and infrastructure scripting.
  • Solid experience with
    Apache Airflow
    for workflow orchestration and data pipeline management.
  • Experience in
    Dockerizing applications
    and working with containerized environments.
  • Knowledge of
    CI/CD pipelines
    using
    GitHub Actions
    or similar tools.
  • Understanding of machine learning lifecycle, model deployment strategies, and production monitoring.
  • Familiarity with security best practices, especially in a
    regulated industry
    like financial services.
  • Strong collaboration and communication skills, with the ability to work effectively across engineering, data, and business teams.

Preferred Qualifications

  • AWS certifications (e.g.,
    AWS Certified Machine Learning – Specialty
    ,
    Solutions Architect
    , or
    DevOps Engineer
    ).
  • Experience working in
    financial services
    or within large enterprise environments.
  • Familiarity with model registry, feature stores, and experiment tracking tools (e.g., MLflow, SageMaker Experiments).
  • Exposure to Infrastructure-as-Code tools such as
    Terraform
    is a plus.

What We Offer

  • Opportunity to work on advanced ML infrastructure and cloud-native solutions with Australia's leading bank.
  • Access to large-scale real-world ML problems and enterprise-grade tooling.
  • Work with a collaborative and innovative team at the forefront of cloud and AI technologies.
  • Access to ongoing learning, certifications, and development resources through Synechron's global programs.

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