Senior AI/ML Platform Engineer

This role focuses on building scalable, reliable, secure, and cost-efficient platforms that help data scientists and ML engineers deploy, monitor, and manage machine learning models in production., * Design, build, and maintain enterprise AI/ML platforms and production MLOps infrastructure.

• Develop and optimize ML CI/CD pipelines, deployment automation, and infrastructure provisioning.
• Manage cloud infrastructure across AWS, Azure, or Google Cloud Platform, with a focus on reliability, scalability, and cost optimization.
• Manage Kubernetes clusters, resource allocation, autoscaling, workload scheduling, and production deployments.
• Implement infrastructure automation using Terraform and Ansible.
• Build monitoring, logging, and observability solutions using Prometheus, Grafana, Datadog, OpenTelemetry, or similar tools.
• Support the complete ML lifecycle, including experiment tracking, model registries, feature stores, model serving, and model drift monitoring.
• Implement automated model testing, validation, canary releases, A/B testing, rollback, and production release strategies.
• Develop reusable SDKs, CLI tools, APIs, deployment templates, and standardized workflows for engineering teams.
• Troubleshoot production incidents, perform root-cause analysis, and improve platform reliability.
• Apply security, governance, responsible AI, and FinOps practices where required.

We are looking for a highly experienced Senior AI/ML Platform Engineer to design, build, maintain, and optimize enterprise AI/ML platforms. The ideal candidate will have strong hands-on experience in platform engineering, MLOps, cloud infrastructure, Kubernetes, CI/CD automation, and production machine learning systems., * 10+ years of experience in platform engineering, MLOps, data engineering, or software engineering.

• At least 2 years of hands-on experience supporting production machine learning systems.
• Strong Python programming and production software development skills.
• Experience building ML CI/CD pipelines and automated deployment frameworks.
• Hands-on experience with AWS, Azure, or Google Cloud Platform.
• Advanced Kubernetes and container orchestration experience.
• Production-scale experience with Terraform and Ansible.
• Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
• Strong understanding of MLOps, model deployment, model monitoring, and the machine learning lifecycle.
• Excellent troubleshooting, incident management, documentation, and communication skills.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline; a master’s degree is preferred., * Experience building shared enterprise platforms or Internal Developer Platforms (IDPs).
• Experience with LLM serving technologies such as vLLM, Text Generation Inference (TGI), or managed LLM APIs.
• Experience in government, regulated, or FedRAMP-compliant environments.
• Familiarity with the NIST AI Risk Management Framework and responsible AI governance.
• Knowledge of FinOps tools and practices, including Infracost and AWS Cost Explorer.
• Experience with GitOps, platform APIs, developer tooling, and infrastructure observability.