ML Engineer

We're looking for a Dev

Ops / ML Engineer who sits at the crossroads of infrastructure, backend development, and machine learning operations. You won't be building ML models from scratch, but you'll need a solid understanding of ML algorithms and pipelines to design, deploy, and maintain the systems that power them. Think of this as an MLOps-flavoured backend role: you'll build CI/CD pipelines, debug ML pipeline failures, propose automation solutions, and keep our production ML systems running smoothly.

Responsibilities:

• Design, build, and continuously improve CI/CD pipelines for both traditional backend services and ML workloads.

• Debug and resolve issues across ML pipelines from data ingestion to model serving, working closely with the data science team.

• Develop and maintain Python-based backend services and tooling that support our ML infrastructure.

• Propose and implement MLOps automation solutions: model versioning, experiment tracking, automated retraining, and monitoring.

• Manage cloud infrastructure (AWS/GCP/Azure), container orchestration (Docker, Kubernetes), and IaC tools (Terraform, Pulumi).

• Monitor production systems, set up alerting, and ensure high availability of ML-powered features.

• Collaborate with data scientists and backend engineers in an agile environment to ship reliable, scalable systems.

Requirements:

• 3-6 years of experience in Dev

Ops, backend engineering, or MLOps roles.

• Strong Python skills: you can write production-grade backend code, not just scripts.

• Solid understanding of ML algorithms and workflows (training, evaluation, and deployment), enough to debug pipeline issues and have informed conversations with data scientists.

• Hands-on experience designing and maintaining CI/CD pipelines (Git

Hub Actions, Git

Lab CI, Jenkins, or similar).

• Experience with containerisation (Docker) and orchestration (Kubernetes).

• Familiarity with ML tooling: MLflow, Kubeflow, Airflow, DVC, or equivalent.

• A proactive, ownership-driven mindset: you identify bottlenecks and propose solutions before being asked.

• Comfort with agile workflows and fast iteration cycles: you thrive in environments where priorities shift, and quality still matters.

Qualifications:

• Bachelor's / Master's Degree in CS / ECE / EE / AI / ML / Data Science.