Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation engagement for one of our clients.
This role is ideal for junior-to-mid-level engineers with strong Google Cloud Platform experience and a focus on building, maintaining, and supporting production-grade machine learning systems.
The selected engineers will work under the direct guidance of a Staff ML Architect and will focus heavily on daily MLOps execution, pipeline maintenance, model reliability, and production support for a high-traffic digital platform.
Required Credentials
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2+ years of experience in machine learning engineering, data engineering, software engineering, or a related technical role.
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Hands-on experience supporting production or near-production ML systems.
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Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience.
Required Qualifications
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Solid hands-on experience with the GCP ecosystem, particularly Vertex AI components such as Workbench, Pipelines, and Model Registry.
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Proficiency with modern ML frameworks, including PyTorch or similar technologies.
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Experience with containerization tools, especially Docker, for automated builds and deployments.
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Practical experience managing data processing workflows using Apache Spark and Airflow.
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Understanding of MLOps best practices, including model deployment, monitoring, training workflows, inference support, and pipeline reliability.
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Familiarity with real-time model serving and infrastructure tools such as Triton Inference Server and Terraform is highly preferred.
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Strong problem-solving skills with the ability to troubleshoot, maintain, and optimize ML pipelines in a production environment.
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Collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.
Key Responsibilities
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Support the design, deployment, monitoring, and maintenance of machine learning models in a high-traffic production environment.
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Maintain, troubleshoot, and optimize end-to-end ML pipelines from raw data ingestion through offline and online model evaluation.
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Execute daily MLOps tasks, including model training, inference support, pipeline monitoring, and deployment maintenance.
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Work with tools such as GCP, Vertex AI, Spark, Airflow, Docker, PyTorch, and related MLOps technologies.
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Build and manage automated containerized deployments to support continuous model operations.
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Partner closely with the Staff ML Architect and other ML Engineers to ensure models are reliable, scalable, and production-ready.
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Help identify and resolve performance, reliability, and scalability issues across ML workflows and infrastructure.
Preferred Qualifications
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Prior experience supporting high-traffic digital platforms or consumer-facing products.
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Experience with Triton Inference Server, Terraform, or similar infrastructure and real-time serving tools.
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Experience working in staff augmentation, consulting, or fast-moving client-facing environments.
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Strong interest in building reliable, production-grade ML systems rather than only experimental or research-focused models.
About Ontrac Solutions
Ontrac Solutions is a strategic consulting and technology solutions firm helping companies Innovate. Create. Elevate. through digital product consulting, cloud solutions, AI-based data solutions, and staff augmentation.
We partner with clients to bring the right technical expertise, execution support, and strategic guidance to complex business and technology initiatives.
