MLOps Platform Architect

Who We Are

At Kyndryl, we design, build, manage and modernize the mission-critical technology systems that the world depends on every day. So why work at Kyndryl? We are always moving forward - always pushing ourselves to go further in our efforts to build a more equitable, inclusive world for our employees, our customers and our communities.

The Role

Kyndryl is seeking an accomplished MLOps Platform Architect to lead the design, build, and continuous operation of a cutting-edge Machine Learning Operations ecosystem. This individual contributor role is pivotal in creating scalable, secure, and performance-optimized platforms that integrate advanced AI and Agentic AI systems, enabling seamless deployment and management of machine learning models across both online and offline environments.

Key Responsibilities:

• Architect, implement, and optimize end-to-end MLOps platforms supporting robust deployment pipelines for diverse ML workloads.
• Design scalable infrastructure for training, serving, and monitoring AI/ML models, leveraging parallel processing, load balancing, and performance tuning techniques.
• Develop and automate CI/CD pipelines incorporating Alpha/Beta testing, Blue/Green deployments, with a focus on deployment automation and production readiness.
• Establish comprehensive model monitoring frameworks to track performance, detect drift, and ensure stability and compliance in production.
• Collaborate closely with data scientists, AI engineers, and platform teams to integrate AI models, support experimental workflows, and enhance platform usability.
• Ensure enterprise-grade platform security, governance, and operational compliance.
• Lead initiatives on platform scalability, cost efficiency, and resource provisioning, maintaining thorough architectural documentation.
• Proactively research and recommend cutting-edge technologies and best practices in MLOps to keep the platform at the forefront of innovation.

• Bachelor's or master's degree in computer science, Engineering, Data Science, or related fields.
• Over 10 years of experience designing and operationalizing complex, scalable MLOps platforms with strong production readiness.
• Deep expertise in AI/ML lifecycle management, deployment automation, performance optimization, and scalable serving frameworks.
• Proficiency with advanced deployment strategies including Alpha/Beta testing, Blue/Green deployments, and load balancing.
• Hands-on experience in parallel processing techniques for scalable training and inference.
• Strong knowledge of modern data ecosystems, containerization (Docker, Kubernetes), orchestration, and cloud AI/ML services (AWS, Azure, GCP).
• Expertise in integrating AI engines and emerging Agentic AI technologies.
• Proven capability as a collaborative team player in cross-functional environments.
• Industry experience in enterprise AI/ML, preferably within financial services or regulated industries.

• Skills in requirements gathering, translating business needs into technical platforms.
• Strong background in DevOps and infrastructure automation practices.
• Broad understanding of data science algorithms and AI model development.
• Advanced cloud architecture expertise and knowledge of data engineering platforms. Excellent communication, organizational, and documentation skills.
• Experience driving platform adoption, user experience improvements, and stakeholder engagement.
• Certifications in cloud platforms and MLOps tools are advantageous.