Senior AI DevOps Architect

Summary

As a Senior AI DevOps Architect you will serve as the architect and strategist, defining the vision, designing the frameworks, and ensuring the long-term success and evolution of the developer experience for our AI/ML engineers. This role is pivotal in optimizing our AI development lifecycle through innovative DevOps practices.

Key Responsibilities

• Developer Experience Design: Proactively identify pain points in the developer journey and architect solutions to streamline workflows, reduce friction, and enhance productivity for AI/ML engineers.
• CI/CD Pipeline Optimization: Design and implement AI-optimized CI/CD pipelines that automate and accelerate build, test, and deployment processes. Focus on predicting and mitigating potential issues to reduce manual intervention and speed up release cycles.
• Code Quality Assurance: Integrate AI-powered tools to automate code reviews, identify errors, vulnerabilities, and style inconsistencies, thereby improving overall code quality and developer efficiency.
• Security Monitoring & Mitigation: Implement AI-driven systems for continuous security monitoring, enabling proactive threat detection and vulnerability mitigation through advanced pattern analysis.
• AI Capabilities Evaluation & Selection: Evaluate and recommend new AI capabilities and tools that can significantly enhance the developer experience and operational efficiency for AI/ML teams.
• Defining Best Practices & Governance: Collaborate with the Platform team to establish organizational standards, security policies, and governance frameworks for AI/ML development and operations.
• Driving Adoption: Develop and execute strategies and initiatives to ensure widespread adoption of MLOps best practices and AI enablement capabilities across all engineering teams.
• Mentorship & Leadership: Guide and mentor intermediate engineers, and provide expert consultation to development teams on complex MLOps challenges, fostering a culture of excellence.

Critical Qualifications

• Strategic Vision & Architecture: Ability to define and articulate a long-term vision for AI/ML developer experience and architect robust, scalable solutions.
• MLOps Expertise: Deep understanding of the end-to-end machine learning lifecycle, including data management, model development, training, deployment, monitoring, and governance.
• CI/CD & Automation: Proven ability to design, implement, and optimize sophisticated CI/CD pipelines specifically for AI/ML workloads, leveraging automation to enhance efficiency and reliability.
• Cloud & Infrastructure Proficiency: Experience with major cloud platforms (AWS,GCP) and associated services relevant to AI/ML development and deployment, including containerization (Docker, Kubernetes) and Infrastructure as Code (Terraform, Ansible).
• AI/ML Tooling: Familiarity with a broad range of AI/ML frameworks, libraries, platforms, and tools (e.g., TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI).
• Security Mindset: Expertise in integrating security best practices throughout the AI/ML development and deployment lifecycle, including threat detection and vulnerability management.
• Problem-Solving & Analytical Skills: Excellent ability to diagnose complex technical challenges, identify root causes, and develop effective, innovative solutions.
• Technical Leadership & Mentorship: Demonstrated capability to lead technical initiatives, guide junior engineers, and provide expert consultation to cross-functional teams.
• Communication & Collaboration: Strong interpersonal and communication skills, with the ability to effectively collaborate with engineering teams, platform teams, and stakeholders, and to drive adoption of new practices and technologies.
• Continuous Learning: A proactive approach to staying abreast of the rapidly evolving landscape of AI, ML, DevOps, and cloud technologies.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
• Minimum of 7-10 years of experience in DevOps, Site Reliability Engineering (SRE), or Software Engineering roles
• At least 3-5 years of direct experience implementing and managing MLOps practices and infrastructure for AI/ML projects.
• Proven track record of designing and architecting scalable CI/CD pipelines for complex applications, preferably including ML models.
• Hands-on experience with container orchestration platforms like Kubernetes.
• Experience with Infrastructure as Code tools (e.g., Terraform, CloudFormation).
• Experience evaluating, selecting, and integrating new tools and technologies to improve developer workflows.
• Experience in defining and enforcing technical standards, policies, and governance frameworks.
• Experience mentoring engineers and leading technical discussions.
• Strong programming skills, particularly in Python.
• Familiarity with monitoring and logging solutions (e.g., Prometheus, Grafana, ELK Stack).
• Knowledge of security best practices in cloud and DevOps environments.
• Understanding of AI/ML concepts and workflows.

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