Intetics

Intetics

Intetics is a top custom software development company with 28 years of experience, offering high-quality software applications and AI/ML integration. They excel in various industries and provide global talent solutions for exceptional project outcomes.

Internet Software & Services
1K-5K
Founded 1995

Description

  • Design and implement scalable, secure, and cost-efficient MLOps solutions on AWS and Databricks.
  • Automate ML deployment pipelines to reduce manual intervention and operational overhead.
  • Collaborate with data scientists to align solutions with MLOps architecture, best practices, and platform standards.
  • Integrate security controls and compliance requirements throughout the machine learning lifecycle.
  • Own and manage incidents end-to-end, including root cause analysis and prevention of recurrence.
  • Contribute to software system architecture and platform-level component design.
  • Build and optimize training, retraining, and inference pipelines for reliability and scalability.
  • Enhance observability through metrics, logging, tracing, and dashboards.
  • Drive infrastructure automation, CI/CD, and cloud resource management best practices across ML teams.

Requirements

  • Strong hands-on experience with AWS architecture, including security best practices, IAM, networking, and cost optimization.
  • Proficiency with Databricks, including MLflow, Workflows, Feature Store, cluster management, and Unity Catalog.
  • Experience with cloud-managed ML platforms such as AWS SageMaker or Google Vertex AI.
  • Expert knowledge of Terraform or 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, and ELK.
  • Strong understanding of GitOps workflows and CI/CD tools such as ArgoCD and FluxCD.
  • Solid knowledge of Docker security, container hardening, and secure container orchestration.
  • Advanced experience in MLOps practices for continuous training, 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 large-scale inference support.
  • Ability to contribute to architecture design, set platform standards, and mentor MLOps or ML engineers.

Interested in this position?

Apply directly on the company website

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