MLOps Engineer

Jobgether • United States

Remote

This Job is No Longer Active This position is no longer accepting applications

AI Summary

This role is focused on building and operating high-performance machine learning inference platforms. The MLOps Engineer will ensure that model serving infrastructure is reliable, scalable, and optimized for latency, throughput, and cost efficiency. The ideal candidate will have 6+ years of experience in distributed systems, infrastructure engineering, or ML platform development.

Key Highlights

Design, build, and operate scalable model serving platforms

Optimize inference performance using techniques such as batching and caching

Implement multi-tenant serving architectures with rate limiting and traffic management controls

Key Responsibilities

Design, build, and operate scalable model serving platforms for LLMs, vision models, and recommendation systems

Optimize inference performance using techniques such as batching, caching, speculative decoding, and request routing strategies

Implement multi-tenant serving architectures with rate limiting, QoS policies, and traffic management controls

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Technical Skills Required

Python Distributed systems Machine learning

Benefits & Perks

Competitive salary range of $100,000 - $150,000 annually

100% remote position within the United States

Full-time W2 employment with long-term stability

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Nice to Have

Experience with AI model serving at scale, multi-region systems, or FinOps optimization

Job Description

This role is focused on building and operating high-performance machine learning inference platforms that support large-scale, production AI systems.You will be responsible for ensuring that model serving infrastructure is reliable, scalable, and optimized for latency, throughput, and cost efficiency.The position sits at the intersection of machine learning and distributed systems engineering, with a strong emphasis on production-grade performance.You will design systems that handle complex workloads such as LLMs, vision models, and recommendation engines across cloud-native environments.

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