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.
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.
