Machine Learning Infrastructure Engineer

Cognizant is seeking a Staff Machine Learning Infrastructure Engineer to lead the technical vision for ML platform initiatives. The role focuses on architecting scalable ML infrastructure, data systems, feature platforms, and serving infrastructure while driving standardization, automation, observability, and cross-functional technical leadership.

Responsibilities:

• Architect end-to-end ML infrastructure spanning data processing, feature management, and model serving
• Design and lead implementation of next-generation ML platforms that support diverse ML workloads
• Drive technical excellence in ML infrastructure through standardization and automation
• Build scalable data processing systems and feature platforms that handle massive-scale ML workloads
• Design robust ML serving architectures supporting both real-time and batch inference
• Establish best practices for ML observability, monitoring, and operational excellence
• Lead cross-functional technical initiatives and mentor platform engineers
• Drive infrastructure decisions that impact the entire ML lifecycle

Requirements:

• 10+ years of software engineering experience, with 5+ years focusing on ML infrastructure and GCP -- must Have to be considered
• 10+ years of software engineering experience, with 5+ years focusing on ML infrastructure
• Deep expertise in distributed systems and data processing at scale
• Strong background in ML platform development and MLOps practices
• Experience building production ML infrastructure supporting critical business applications
• Proven track record of leading complex technical initiatives
• Expert-level knowledge in:
• Large-scale data processing systems (Spark, Beam)
• Feature store architectures and implementations
• ML serving platforms and inference optimization (TorchServe, Tensorflow Serving and Triton)
• Container orchestration and cloud platforms
• Data pipeline design and optimization
• ML system monitoring and observability
• Feature stores and feature computation systems
• Data quality and validation frameworks
• Dataset versioning and lineage tracking
• Efficient data storage and access patterns
• Model deployment and serving platforms
• Inference optimization and scaling
• Load balancing and traffic management
• Model versioning and lifecycle management
• MLOps tooling and automation
• Experimentation platforms
• Monitoring and observability systems
• Resource management and optimization
• This is a remote position open to qualified applicants in the United States
• Experience with GPU infrastructure and optimization
• Background in high-performance computing
• Contributions to open-source ML infrastructure projects
• Experience with ML-specific security and compliance requirements
• Master's degree in computer science or related field