Senior AI Platform Engineer

OpsWerks is a technical consulting company specializing in operational services for the high-tech industry. We help platform and infrastructure teams operate multi-cloud environments, execute complex migrations, and enable seamless app deployments.

Your Role

As a Senior AI Platform Engineer, you will be responsible for operating, maintaining, and continuously improving the company’s AI platforms running on Kubernetes (On-premise and/or on AWS/GCP) - similar on the AIoEKS (AI on EKS) deployment frameworks and Kubeflow’s Machine Learning Toolkit 

Platform Ownership & Operations 

• Deploy new releases and configuration changes through GitOps/DevOps

• Monitor platform and service health using logs, metrics, and observability tools  

• Improve platform observability, operational tooling/automations, self-service capabilities and reliability practices to reduce recurring issues 

• Participate in incident response, root cause analysis and 24x7 operational rotations 

User & Developer Experience 

• Investigate & troubleshoot user concerns by either correlating them to system-related issues, breaking integrations and/or user-specific errors/misconfigurations up to recommending/executing resolutions

• Advocate for platform standards, security best practices, and operational excellence 

Collaboration and Leadership 

• Provide structured Python mentorship to junior engineers, focusing on strong fundamentals and bridge foundational Python knowledge toward MLOps competencies 

• Lead the adoption of MLOps best practices for the team 

• Influence the team roadmap by identifying gaps in tooling, skills, and processes required to support production-grade AI systems 

Your Qualifications

• 3+ years of experience supporting production workloads/platforms (Ray.IO, Jupyter Notebooks, AWS SageMaker, Kubeflow AI Tools or an AI-related equivalent) 

• 5+ years of hands-on experience AI/ML lifecycle (development/deployment, DevOps/MLOps)

• 5+ years of Python experience in development & support on AI/ML workflows and data engineering pipelines 

• Practically skilled in Kubernetes environments including Cloud-provider managed Kubernetes flavors (AWS-EKS/GCP-GKE)

• Knowledge on microservice architectures and service communication patterns 

• Strong troubleshooting fundamentals such as application crashes, resource contentions, service latency, and scaling behavior 

• Well-rounded competency in analyzing logs, metrics, monitoring systems, and service KPIs 

Plus points if you have:

• Exposure in other Data/AI platforms such as Flyte, HuggingFace & AI Agent Platforms (Vertex AI, Claude Code, LangChain, etc...) 

• Hands-on experience with automation or scripting (Bash, Python)

• Kubernetes or cloud certifications (CKAD, AWS)