Machine Learning Infrastructure Engineer

Cognizant

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Francisco, CA
Posted
11 days ago
Freshness
Confirmed live yesterday

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Similar $223k
$163k $290k
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About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 330 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 211 roles with salary data.

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At a glance

TL;DR · Machine Learning Infrastructure Engineer

JOB TITLE: Machine Learning Infrastructure Engineer The Staff Machine Learning Infrastructure Engineer joins the ML Platform Engineering department to architect and lead the technical vision for scalable ML platform initiatives. This role involves designing end-to-end infrastructure for data processing, feature management, and model serving to support diverse ML workloads. You will build scalable data processing systems, feature platforms, and robust serving architectures for real-time and batch inference. Key responsibilities include establishing best practices for ML observability, monitoring, and operational excellence while mentoring platform engineers. Required expertise includes distributed systems, MLOps practices, and large-scale data processing using Spark and Beam. You must also possess expert-level knowledge in feature store architectures, TorchServe, Tensorflow Serving, Triton, container orchestration, and cloud platforms to solve complex infrastructure problems across the entire ML lifecycle.

What you'll do

  • Architect end-to-end ML infrastructure spanning data processing, feature management, and model serving.
  • Design and lead the implementation of next-generation ML platforms for diverse workloads.
  • Build scalable data processing systems and feature platforms for massive-scale ML workloads.
  • Design robust ML serving architectures for both real-time and batch inference.
  • Establish best practices for ML observability, monitoring, and operational excellence.
  • Define the technical strategy, roadmap, and architectural decisions for complex ML systems.
  • Build frameworks and platforms that accelerate ML development and deployment at scale.
  • Lead design reviews and provide technical mentorship to platform engineers.

What we're looking for

  • 10+ years of software engineering experience.
  • 5+ years of experience focusing on ML infrastructure and GCP.
  • Deep expertise in distributed systems and data processing at scale.
  • Strong background in ML platform development and MLOps practices.
  • Expert-level knowledge in Spark, Beam, feature stores, and ML serving platforms like TorchServe, Tensorflow Serving, or Triton.
  • Experience building production ML infrastructure supporting critical business applications.
  • Experience with GPU infrastructure and optimization (preferred).
  • Master's degree in Computer Science or related field (preferred).

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