Ml Ops Engineering Lead

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 Sage Maker , 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 Cloud Formation.
Containerize applications using Docker and build robust CI / CD pipelines with Git Hub 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 Experience4–7+ years of experience in MLOps, Cloud Engineering, or Machine Learning Engineering.
Strong hands-on experience with AWS services , particularly Sage Maker , Glue , EC2 , Lambda , and Cloud Formation.
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 Git Hub 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 Dev Ops Engineer ).
Experience working in financial services or within large enterprise environments.
Familiarity with model registry, feature stores, and experiment tracking tools (e.g., MLflow, Sage Maker 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.
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer.
Our Diversity, Equity, and Inclusion (DEI) initiative 'Same Difference' is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all.
We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company.
We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply.
We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant's gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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