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Full Time Mlops Engineer Jobs (NOW HIRING)

Nace AI

Senior MLOps Engineer

Palo Alto, CA · On-site

$122K - $168K/yr

Palo Alto, CA | Full-Time | On-site About Nace AI: Nace AI is an enterprise AI product and research ... As a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI's models from ...

CGI Inc.

Senior ML/MLOps Engineer

Pittsburgh, PA · On-site

$101K - $139K/yr

Senior ML/MLOps Engineer Category: Analytics and Emerging Digital Technologies Main location ... J0826-1360 Employment Type: Full Time Position Description: This role will require someone at our ...

Capital One

Lead Software Engineer - MLOps

Mclean, VA · On-site

Lead Software Engineer - MLOps Do you love building and pioneering in the technology space? Do you ... The minimum and maximum full-time annual salaries for this role are listed below, by location.

FutureFit AI

ML Ops Lead

$104K - $138K/yr

Staff / Principal MLOps Engineer Full-Time | Remote (US or Canada) or Contract (6 months, potential to convert) Come join our Data team! High velocity, high trust, and high impact with a will to win.

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Full Time Mlops Engineer information

What is the difference between Full Time Mlops Engineer vs Data Scientist?

AspectFull Time Mlops EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; experience with ML pipelinesBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentFocus on deploying, maintaining ML models, infrastructure, automationFocus on data analysis, model development, insights generation
Employer & Industry UsageTech companies, AI startups, enterprises with ML productsResearch institutions, tech firms, finance, healthcare

Full Time Mlops Engineers primarily focus on deploying and maintaining machine learning models in production environments, emphasizing infrastructure and automation. Data Scientists concentrate on analyzing data, developing models, and deriving insights. While both roles require a strong understanding of machine learning, MLOps engineers are more involved in the operational aspects, whereas Data Scientists focus on model creation and analysis.

Are full time MLOps engineers in demand?

Full-time MLOps engineers are in high demand due to the increasing adoption of machine learning and AI across industries. Companies seek professionals skilled in cloud platforms, automation, and tools like Docker, Kubernetes, and CI/CD pipelines to deploy and maintain ML models efficiently.

Are full time MLops engineers still in demand?

Full-time MLOps engineers are currently in high demand due to the increasing adoption of machine learning models in various industries. They are needed to develop, deploy, and maintain scalable AI systems, often requiring skills in cloud platforms, containerization, and automation tools. The role is expected to grow as organizations prioritize operationalizing AI solutions efficiently.

How much do full time MLOps engineers make?

Full-time MLOps engineers typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and automation tools can earn higher salaries, often exceeding $160,000 per year.
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Infographic showing various Full Time Mlops Engineer job openings in the United States as of August 2026, with employment types broken down into 93% Full Time, 3% Part Time, and 4% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution.

Senior MLOps Engineer

Nace AI

Palo Alto, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 18 days ago

Job description

Palo Alto, CA | Full-Time | On-site
About Nace AI:
Nace AI is an enterprise AI product and research company in Palo Alto (backed by General Catalyst, Walden Catalyst, and Intel). We build long-running AI agents powered by our own specialized SLMs - we started with financial audit and accounting workflows and are expanding from there. Real enterprise deployments, not demos.
Role Overview:
As a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI's models from research to reliable, production-grade systems. Our infrastructure generates task-specific Small Language Models (SLMs) in real time - which means our training, serving, and evaluation infrastructure isn't an afterthought; it is the product. You will design and operate the pipelines, orchestration, and serving layers that allow us to train, deploy, monitor, and continuously improve many specialized models at once, with the reliability that high-stakes audit, compliance, and finance workflows demand. This role sits at the intersection of ML engineering, LLM inference infrastructure, and platform reliability, and requires both strong systems instincts and hands-on execution.
Key Responsibilities:
  • Design, build, and operate end-to-end ML infrastructure: training orchestration, experiment tracking, model registries, CI/CD for models, and automated evaluation pipelines.
  • Own LLM/SLM serving infrastructure - scale low-latency, high-throughput inference using frameworks like vLLM, including batching, caching, and autoscaling strategies.
  • Build and manage multi-GPU training and inference clusters (scheduling, utilization, cost optimization) across cloud and on-prem environments.
  • Implement observability for models in production: latency, throughput, drift, regression, and quality monitoring with actionable alerting.
  • Apply inference-time optimizations - quantization (AWQ, GPTQ, FP8/GGUF), distillation support, KV-cache management, and deployment tuning - in partnership with our ML and Research Engineers.
  • Harden our stack for enterprise deployment: reproducibility, versioning, access controls, and audit-ready traceability of model behavior.
  • Set MLOps best practices and tooling standards as an early, senior member of the infrastructure team.

Qualifications:
  • 5+ years of experience in MLOps, ML infrastructure, or platform engineering, with substantial production ownership.
  • Proven experience deploying and scaling LLM, inference infrastructure in production, including model serving frameworks such as TRT, vLLM, SGLang or TGI.
  • Strong proficiency with Kubernetes, containerization (Docker), and infrastructure-as-code (Terraform or similar).
  • Hands-on experience with GPU cluster management and distributed training/serving environments.
  • Proficient in Python with a strong track record of building substantial, maintainable systems.
  • Experience with ML pipeline and orchestration tooling (e.g., Airflow, Kubeflow, Ray, MLflow, Weights & Biases).
  • Solid foundation in computer science fundamentals and cloud architecture (AWS, GCP, or Azure).
  • BS degree in CS or related technical field.
  • Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:
  • MS in CS or related technical field.
  • Experience operating multi-node GPU training infrastructure.
  • Hands-on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF) and other inference-time optimizations.
  • Familiarity with data processing stacks such as Spark and Airflow.
  • Experience supporting fine-tuning workflows for LLMs/VLMs (instruction tuning, RLHF/DPO pipelines).
  • Experience in regulated or enterprise environments where reliability, security, and auditability are first-class requirements.
  • Contributor to open-source ML infrastructure projects.

Why Nace AI?
  • Pedigree: Work with a team from top-tier institutions and companies, backed by the best VCs in the world.
  • Impact: You are joining early enough to shape the infrastructure foundations of a company aiming to be the "OS" for professional knowledge.
  • Competitive Package: Silicon Valley-standard salary, significant equity, and premium benefits.

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