Senior MLOps / AI Ops Engineer

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

Key Responsibilities

• Design, implement, and optimize MLOps/AI Ops architectures supporting scalable AI/ML solutions.
• Build and manage cloud-native infrastructure using Azure, Kubernetes, Docker, Terraform, and CI/CD pipelines.
• Lead end-to-end AI/ML lifecycle operations, including data ingestion, model training, versioning, deployment, and monitoring.
• Implement and manage agent/model registries, automated deployment workflows, and model monitoring systems.
• Drive AI Ops capabilities, including prompt lifecycle management, RAG pipeline optimization, semantic search enhancements, vector database integration, and LLM observability.
• Establish robust monitoring, alerting, and performance tuning practices for large-scale production systems.
• Collaborate with data engineering teams to ensure efficient data pipelines and infrastructure alignment.
• Ensure high standards of security, governance, and reliability across ML/AI systems.
• Implement security best practices such as IAM, secrets management, policy-as-code, drift detection, and audit trails.
• Optimize production systems for autoscaling, resiliency, and cost efficiency.
• Evaluate emerging technologies and recommend enhancements to the AI/ML platform.

• 12+ years of overall experience, with strong expertise in MLOps, AI Ops, and cloud infrastructure.
• Hands-on experience with Kubernetes, Docker, Terraform, Azure cloud, and CI/CD pipelines.
• Strong understanding of the full AI/ML lifecycle, including data engineering, model development, deployment, monitoring, and retraining.
• Experience with LLM lifecycle, prompt management, RAG frameworks, vector databases, semantic caching, and observability tools.
• Solid understanding of infrastructure-as-code, automation, and cloud-native architectures.
• Knowledge of IAM, secrets management, security policies, audit logging, and compliance frameworks.
• Proven ability in performance tuning, autoscaling, and managing large-scale distributed systems.
• Excellent communication, collaboration, and leadership skills.

• Experience with reliability engineering, SRE practices, and cost-optimization strategies.
• Exposure to monitoring tools and frameworks for AI/ML observability.
• Certifications in Azure, Kubernetes, DevOps, or AI/ML.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.