1062 | MLOps Engineer

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced MLOps Engineer to join our dynamic team on a full-time basis.

Responsibilities:

• Design and implement scalable, secure, and cost‑efficient MLOps solutions leveraging AWS and Databricks.
• Automate ML deployment pipelines, reducing manual intervention and operational overhead.
• Collaborate closely with data scientists to ensure solutions align with established MLOps architecture, best practices, and platform standards.
• Integrate security controls and compliance requirements throughout the entire machine learning lifecycle.
• Own and manage incidents end‑to‑end, from root cause analysis to prevention of future occurrences.
• Contribute to software system architecture and the design of platform‑level components.
• Build and optimize ML training, retraining, and inference pipelines, ensuring reliability and scalability.
• Enhance observability with metrics, logging, tracing, and dashboards to ensure system visibility and performance.
• Drive best practices in infrastructure automation, CI/CD, and cloud resource management across ML teams.

Requirements:
• Strong hands‑on experience with AWS architecture, including security best practices, IAM, networking, and cost optimization.
• Proficiency with Databricks (essential): MLflow, Workflows, Feature Store, cluster management, Unity Catalog.
• Experience with cloud‑managed ML platforms such as AWS SageMaker or Google Vertex AI.
• Expert knowledge of Terraform / Terragrunt for multi‑cloud infrastructure provisioning and automation.
• Deep expertise in Kubernetes, including autoscaling, GPU workloads, networking policies, and cluster optimization.
• Practical experience with observability stacks such as Prometheus, Grafana, Loki, ELK.
• Strong understanding of GitOps workflows and CI/CD tools (e.g., ArgoCD, FluxCD).
• Solid knowledge of Docker security, container hardening, and secure container orchestration.
• Advanced experience in MLOps practices for continuous training (CT), CI/CD for ML models, and automated deployment.
• Familiarity with ML pipeline orchestration tools such as Kubeflow or Argo Workflows.
• Experience with LLMOps, including frameworks such as Langfuse, ollama, vLLM, and supporting large‑scale inference.
• Ability to contribute to architecture design, set platform standards, and mentor MLOps or ML engineers.