Job Description

About the Role

Synechron 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

• 4–7+ 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.

Ready to build the future of AI in banking?

• Apply today and be a part of cloud and ML transformation journey with Synechron.