AI/ML Platform Engineer

First Soft Solutions LLC; Baltimore (MD); On-site; USD 150,000 - 230,000; Full time

Job summary

First Soft Solutions LLC seeks a Senior AI/ML Platform Engineer to design and operate enterprise AI/ML infrastructure, automate MLOps workflows, and improve platform reliability.

You will drive production ML systems with strong focus on scalable deployments, monitoring and incident response, collaborating across teams to ensure robust, compliant ML platforms.

Qualifications

• 10+ years in platform/MLOps/software engineering, incl. 2+ years with production ML systems.
• Proficient in Python, ML CI/CD, deployment automation and production software dev.
• Experience with AWS, Azure, or GCP.
• Advanced Kubernetes: cluster management, autoscaling, quotas and scheduling.
• Monitoring with Prometheus, Grafana, Datadog or OpenTelemetry.
• IaC with Terraform and Ansible.
• ML lifecycle: experiment tracking, model registries, feature stores, model serving, drift monitoring.
• Automated model testing, A/B testing, canary deployments, rollback.
• SDKs, CLI tools, APIs, deployment templates, and standardized workflows.
• Troubleshooting, incident response, root-cause analysis and documentation.

Responsibilities

• Design and operate enterprise AI/ML infrastructure.
• Automate MLOps workflows and improve platform reliability.
• Collaborate with teams to scale production ML systems.

Skills

Platform/MLOps

Python

ML CI/CD

Cloud platforms

Kubernetes

Monitoring

Terraform

ML lifecycle

Model serving

A/B testing

Tools

Prometheus

Grafana

Datadog

OpenTelemetry

Terraform

Ansible

Job description

Job Description

Seeking a Senior AI/ML Platform Engineer to design and operate enterprise AI/ML infrastructure, automate MLOps workflows, and improve platform reliability.

Required Skills

• 10+ years in platform/MLOps/software engineering, including 2+ years with production ML systems.
• Python, ML CI/CD, deployment automation, and production software development.
• AWS, Azure, or GCP.
• Advanced Kubernetes: cluster management, autoscaling, resource quotas, and scheduling.
• Prometheus, Grafana, Datadog, or OpenTelemetry.
• Terraform, Ansible, and infrastructure automation.
• ML lifecycle, experiment tracking, model registries, feature stores, model serving, and drift monitoring.
• Automated model testing, A/B testing, canary deployments, and rollback.
• SDKs, CLI tools, APIs, deployment templates, and standardized workflows.
• Troubleshooting, incident response, root-cause analysis, and documentation.

Preferred

Internal Developer Platforms, vLLM, TGI, managed LLM APIs, FedRAMP, NIST AI RMF, GitOps, Infracost, AWS Cost Explorer, and FinOps.