About this role
We're looking for a Senior MLOps Engineer to own how our AI & Data Science team builds, ships, and runs machine learning systems on AWS. You'll be a technical SME for our data science and ML engineers - owning the path from experimentation through to reliable, productionised ML systems, with a focus on lifting lifecycle maturity, reducing technical debt, remediating security risks, and enabling the team to move faster with confidence.
You’ll:
Provide technical leadership: Serve as the key technical SME for data scientists and ML engineers on technical problems, including architecture, tooling, debugging, and production design.Own the experimental lifecycle: Architect and support the transition from data science experimentation to productionised ML, ensuring reproducible pipelines, consistent evaluation, and seamless handoffs.Systematize ML delivery: Set shared patterns for how models are deployed, versioned, and released so that shipping is repeatable and proven solutions are reusable and scalable.Establish closed-loop experimentation: Build a clear line of sight from live model telemetry back to the experimentation phase, ensuring production performance consistently drives the next iteration of model refinement.Reduce cost and remediate risk: Keep ML infrastructure and model-serving costs appropriate through rightsizing, caching, and consolidated spend visibility. Proactively identify and remediate security risks (e.g., dependency vulnerabilities, access and credential hygiene, and secure deployment practices) by partnering with security teams.Share knowledge and contribute: Help engineers grow through hands-on technical guidance, pairing, and constructive review. Foster a collaborative environment built on mutual respect and trust by actively contributing your ideas and expertise to the wider team. What we’re looking for:
We’re looking for someone who brings a curious mindset, a collaborative spirit, and deep technical expertise. You are someone who naturally aligns with our values - you step in, you own the outcomes, and you change the game
Strong familiarity with building, shipping, and maintaining robust machine learning systems on AWS (e.g., SageMaker, EC2, EKS, Lambda).5+ years of experience across MLOps, DevOps, or Software Engineering, combining a strong understanding of the end-to-end data science lifecycle with a primary focus on scaling machine learning infrastructure and productionising models.Sound knowledge of industry-standard MLOps tooling, modern CI/CD practices, model registries, and orchestration platforms (e.g., Metaflow, MLflow, Terraform, Kubernetes).Proven track record of building event-driven or distributed systems, applying appropriate engineering methods (e.g., modularisation, abstractions) that make assets tractable and reusable.A proactive approach to cloud cost optimization, infrastructure security, and access management (IAM), coupled with a passion for clean code and continuous improvement.We know not everyone will meet every requirement, and that’s okay. If you’re excited about the role and believe you can make a difference, we’d love to hear from you.
A few other things we want you to know...
As an equal opportunity employer, carsales welcomes applications from individuals of all backgrounds, identities, abilities, and life experiences. We are committed to fostering a diverse and inclusive workplace where everyone feels valued and supported. If you have a disability or require adjustments to participate fully in the recruitment process, please let us know - we’re here to support you. We understand the importance of work-life balance and offer flexible working arrangements, including part-time options. If you’d like to explore this, just ask during the process. We’re also a proud Circle Back Initiative employer, which means we commit to responding to every applicant. Successful candidates joining the carsales team will need to complete a National Police Record check and must have full-time Australian working rights. So come and join our team - because every role is a big role in our plans to go big.