Senior AI/ML Platform Engineer
Heptaware Global
Senior AI/ML Platform Engineer
Location: MD (Onsite)
Rate: $45/hr for sub C2C
Experience: 10+ Years
Openings: 1
Job Description:
Seeking a Senior AI/ML Platform Engineer to develop and maintain enterprise AI/ML infrastructure, automate MLOps workflows, manage Kubernetes environments, and ensure platform reliability and performance.
Required Skills:
10+ years in platform engineering, MLOps, or software/data engineering, including 2+ years with production ML systems.
Strong Python and production software engineering.
ML CI/CD pipelines, automation, deployment, and release management.
AWS, Azure, or GCP cloud infrastructure and cost optimization.
Advanced Kubernetes: cluster administration, autoscaling, resource quotas, and workload scheduling.
Prometheus, Grafana, Datadog, OpenTelemetry, or equivalent monitoring.
Terraform, Ansible, and infrastructure automation.
ML lifecycle, experiment tracking, model registries, feature stores, model serving, and drift monitoring.
Automated model validation, A/B testing, canary deployments, rollback, and production releases.
Internal SDKs, CLI tools, APIs, reusable templates, and standardized deployment workflows.
Incident response, troubleshooting, root-cause analysis, and documentation.
Preferred: Internal Developer Platforms (IDPs), vLLM, Text Generation Inference (TGI), managed LLM APIs, FedRAMP, government experience, NIST AI RMF, GitOps, Infracost, AWS Cost Explorer, and FinOps.
Location: MD (Onsite)
Rate: $45/hr for sub C2C
Experience: 10+ Years
Openings: 1
Job Description:
Seeking a Senior AI/ML Platform Engineer to develop and maintain enterprise AI/ML infrastructure, automate MLOps workflows, manage Kubernetes environments, and ensure platform reliability and performance.
Required Skills:
10+ years in platform engineering, MLOps, or software/data engineering, including 2+ years with production ML systems.
Strong Python and production software engineering.
ML CI/CD pipelines, automation, deployment, and release management.
AWS, Azure, or GCP cloud infrastructure and cost optimization.
Advanced Kubernetes: cluster administration, autoscaling, resource quotas, and workload scheduling.
Prometheus, Grafana, Datadog, OpenTelemetry, or equivalent monitoring.
Terraform, Ansible, and infrastructure automation.
ML lifecycle, experiment tracking, model registries, feature stores, model serving, and drift monitoring.
Automated model validation, A/B testing, canary deployments, rollback, and production releases.
Internal SDKs, CLI tools, APIs, reusable templates, and standardized deployment workflows.
Incident response, troubleshooting, root-cause analysis, and documentation.
Preferred: Internal Developer Platforms (IDPs), vLLM, Text Generation Inference (TGI), managed LLM APIs, FedRAMP, government experience, NIST AI RMF, GitOps, Infracost, AWS Cost Explorer, and FinOps.
Reference: 3205802586