Machine Learning Infrastructure Engineer
cognizantMiami (FL)
About the role
*NO Visa sponsorship / transfer/ c2c available Machine Learning Infrastructure Engineer Job Type: Full-time - Visa independent only**Department: ML Platform Engineering
About the Role:
As a Staff Machine Learning Infrastructure Engineer, you will architect and lead the technical vision for our ML platform initiatives, focusing on building scalable infrastructure that powers our ML capabilities. You will design and drive the evolution of our ML platforms, data systems, and serving infrastructure that enable teams to efficiently develop, deploy, and operate ML models at scale.
Key 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
- Technical Leadership:
- Define technical strategy and roadmap for ML infrastructure
- Drive architectural decisions for complex ML systems
- Lead design reviews and provide technical mentorship
- Collaborate with data science teams to understand and address infrastructure needs
- Establish standards for reliability, scalability, and performance
- Build frameworks and platforms that accelerate ML development
Required Qualifications:
- 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 implementationsML serving platforms and inference optimization (TorchServe, Tensorflow Serving and Triton)
- Container orchestration and cloud platforms
- Data pipeline design and optimizationML system monitoring and observability
- Technical Expertise:
- Data Infrastructure:
- Feature stores and feature computation systems
- Data quality and validation frameworks
- Dataset versioning and lineage tracking
- Efficient data storage and access patterns
- Serving Infrastructure:
- Model deployment and serving platforms
- Inference optimization and scaling
- Load balancing and traffic management
- Model versioning and lifecycle management
- Platform Development:
- MLOps tooling and automation
- Experimentation platforms
- Monitoring and observability systems
- Resource management and optimization
Preferred Qualifications:
- 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
Impact:
Shape the technical direction of ML infrastructure across the organization
Drive innovation in ML platforms and tools
Mentor and grow the technical capabilities of the team
Establish architectural patterns and best practices
Enable rapid ML development and deployment at scale
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