Senior Mlops Engineer Jobs
Senior Mlops Engineer jobs are open across technology, finance, healthcare, and retail, from mid-level to staff and principal, with specializations in model deployment, pipeline automation, and ML infrastructure. Find a role that fits from the openings below and apply directly.
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Showing 4 of 17+ Senior Mlops Engineer jobs
The Opportunity:
As a Senior MLOps Engineer, you will build, deploy, and operate machine learning and generative AI solutions in enterprise environments. Working closely with AI leads, ML engineers, and platform teams, you will turn experimental workflows into production-ready services and establish the operational practices needed to support them. You will report to the AI Team Leader, Technology Innovation.
The Day-to-Day:
- Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks, including ingestion, validation, feature engineering, model training, fine-tuning, evaluation, and deployment
- Build and maintain CI/CD workflows for application code, model artifacts, prompt templates, retrieval and context configurations, evaluation datasets, and infrastructure
- Operationalize retrieval-augmented generation (RAG) and context engineering workflows, including document ingestion, chunking, embedding generation, indexing, retrieval, context selection and assembly, and evaluation of retrieval and response quality
- Collaborate with ML engineers, data engineers, and the AI Lead to translate experimental training, fine-tuning, evaluation, and inference workflows into reproducible, production-ready systems
- Develop and maintain production-grade APIs and microservices using Python and FastAPI to expose model inference and integrate AI capabilities into enterprise applications
- Deploy, operate, and scale model-serving endpoints and containerized inference services using Azure-managed services, Docker, and Kubernetes to workload requirements
- Provision and manage cloud and platform resources through infrastructure as code, ensuring consistent, repeatable, and auditable environment configurations
- Implement automated testing and release quality gates for application code, data quality, model performance, retrieval quality, AI safety, and deployment readiness
- Establish model and artifact versioning, experiment tracking, and deployment traceability to support reproducibility and troubleshooting
- Monitor and improve model quality, data and retrieval pipeline reliability, inference latency, throughput, service availability, and infrastructure cost efficiency
- Partner with infrastructure, security, and governance teams to implement appropriate access controls, auditability, and operational safeguards
- 10+ years of experience building data-driven software solutions, including 5+ years of hands-on machine learning engineering experience
- 8+ years of Python development experience, including building and maintaining production services, APIs, or data pipelines
- Demonstrated experience deploying and operating machine learning or generative AI solutions in production
- Experience with Databricks, including Delta Lake, Auto Loader, Workflows, Feature Engineering, and deployment automation using Databricks Asset Bundles
- Hands-on experience building CI/CD pipelines and implementing automated testing and deployment workflows for data and ML systems
- Hands-on experience with cloud-native AI infrastructure and related technologies, including Azure Foundry, Azure Kubernetes Service, Docker, and GPU-backed workloads
- Hands-on experience integrating and operating graph databases to support knowledge graphs, relationship-based retrieval, or generative AI applications
- Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience
- Experience deploying models for low-latency, high-throughput inference using frameworks such as vLLM and other GPU-based model-serving toolsExperience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS
We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like:
- 100% paid medical, dental and vision premiums for you and your qualifying dependents
- A 50% 401(k) match, up to the IRS maximum
- 20 days of PTO, plus 10 paid holidays
- Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care
- This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change.
The Opportunity:
As a Senior MLOps Engineer, you will build, deploy, and operate machine learning and generative AI solutions in enterprise environments. Working closely with AI leads, ML engineers, and platform teams, you will turn experimental workflows into production-ready services and establish the operational practices needed to support them. You will report to the AI Team Leader, Technology Innovation.
The Day-to-Day:
- Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks, including ingestion, validation, feature engineering, model training, fine-tuning, evaluation, and deployment
- Build and maintain CI/CD workflows for application code, model artifacts, prompt templates, retrieval and context configurations, evaluation datasets, and infrastructure
- Operationalize retrieval-augmented generation (RAG) and context engineering workflows, including document ingestion, chunking, embedding generation, indexing, retrieval, context selection and assembly, and evaluation of retrieval and response quality
- Collaborate with ML engineers, data engineers, and the AI Lead to translate experimental training, fine-tuning, evaluation, and inference workflows into reproducible, production-ready systems
- Develop and maintain production-grade APIs and microservices using Python and FastAPI to expose model inference and integrate AI capabilities into enterprise applications
- Deploy, operate, and scale model-serving endpoints and containerized inference services using Azure-managed services, Docker, and Kubernetes to workload requirements
- Provision and manage cloud and platform resources through infrastructure as code, ensuring consistent, repeatable, and auditable environment configurations
- Implement automated testing and release quality gates for application code, data quality, model performance, retrieval quality, AI safety, and deployment readiness
- Establish model and artifact versioning, experiment tracking, and deployment traceability to support reproducibility and troubleshooting
- Monitor and improve model quality, data and retrieval pipeline reliability, inference latency, throughput, service availability, and infrastructure cost efficiency
- Partner with infrastructure, security, and governance teams to implement appropriate access controls, auditability, and operational safeguards
- 10+ years of experience building data-driven software solutions, including 5+ years of hands-on machine learning engineering experience
- 8+ years of Python development experience, including building and maintaining production services, APIs, or data pipelines
- Demonstrated experience deploying and operating machine learning or generative AI solutions in production
- Experience with Databricks, including Delta Lake, Auto Loader, Workflows, Feature Engineering, and deployment automation using Databricks Asset Bundles
- Hands-on experience building CI/CD pipelines and implementing automated testing and deployment workflows for data and ML systems
- Hands-on experience with cloud-native AI infrastructure and related technologies, including Azure Foundry, Azure Kubernetes Service, Docker, and GPU-backed workloads
- Hands-on experience integrating and operating graph databases to support knowledge graphs, relationship-based retrieval, or generative AI applications
- Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience
- Experience deploying models for low-latency, high-throughput inference using frameworks such as vLLM and other GPU-based model-serving toolsExperience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS
We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like:
- 100% paid medical, dental and vision premiums for you and your qualifying dependents
- A 50% 401(k) match, up to the IRS maximum
- 20 days of PTO, plus 10 paid holidays
- Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care
- This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change.
Senior Mlops Engineer Jobs by Experience Level
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Find Senior Mlops Engineer JobsSenior Mlops Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
What Employers Look For
The qualifications that appear most often in senior mlops engineer jobs.
- 3 or more years of experience building and maintaining production ML pipelines
- Proficiency with orchestration tools such as Airflow, Kubeflow, or Prefect
- Hands-on experience with at least one major cloud platform: AWS, GCP, or Azure
- Strong Python skills including packaging, testing, and environment management
- Experience with containerization and Kubernetes for model serving and scaling
- Familiarity with CI/CD practices applied to machine learning workflows and model registries
Tips for Your Senior Mlops Engineer Job Search
Quantify your deployment impact on resumes
Recruiters for senior mlops engineer roles want numbers tied to reliability and scale. Swap vague phrases like 'improved pipelines' for specific outcomes: reduced model deployment time, increased uptime, or cut infrastructure costs by a measurable margin.
Tailor your stack to each job description
MLOps toolchains vary widely. One employer runs Kubeflow on GCP, another uses SageMaker Pipelines with Airflow. Mirror the exact tools named in the posting so your resume clears both automated filters and recruiter eyeballs on the first pass.
Apply early to roles that fit
Migrate Mate lists senior mlops engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that name your ML frameworks
Senior mlops engineers are often hired to own a specific framework ecosystem. Filter openings by the frameworks you know deepest, whether that is PyTorch serving, TensorFlow Extended, or Ray, so your application lands where you can immediately add value.
Prepare a system design answer for the interview loop
Almost every senior mlops engineer interview includes a live design session: design a feature store, a model registry, or a retraining pipeline. Practice narrating trade-offs aloud, covering observability, latency, and failure modes, not just the happy path.
Negotiate with total comp context in mind
Offers for senior mlops engineers often differ more in equity, cloud credits, and compute allowances than in base pay. Before you respond to an offer, ask explicitly what the equity vesting schedule looks like and whether a compute or tooling budget is included.
Senior Mlops Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior mlops engineers?
The companies hiring the most senior mlops engineers right now include Grindr, TRM Labs, and Fisher Investments, with the largest share of openings in California, Texas, and Washington, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated at companies running large-scale model inference in production.
How many senior mlops engineer jobs are remote?
About 75% of senior mlops engineer openings are fully remote or hybrid as of November 2026, making it one of the more remote-accessible senior engineering roles. Model monitoring, pipeline development, and infrastructure-as-code work tend to be the sub-areas most commonly approved for fully distributed arrangements.
How do you become a senior mlops engineer?
Start by building production experience with ML pipelines, not just experimentation notebooks. Work toward owning deployment, monitoring, and retraining loops end to end. Deepen expertise in one cloud platform and one orchestration tool, then demonstrate that you can reduce manual intervention in model lifecycle management through automation and observability tooling.
Can you get hired as a senior mlops engineer without direct MLOps experience?
Yes, especially if you come from a strong DevOps or data engineering background and can show that you have applied those skills to ML systems. Employers often promote internally from ML engineering or platform engineering when a candidate understands both the software reliability side and the model lifecycle side of the role.
What does the senior mlops engineer interview process look like?
Most loops include a recruiter screen, a technical phone interview covering Python and pipeline concepts, a system design round where you architect a complete ML platform component, and a final round with cross-functional stakeholders. Some employers also include a take-home that asks you to debug or extend an existing ML workflow before the onsite stage.
Where can I find and apply to senior mlops engineer jobs?
You can find and apply to senior mlops engineer jobs on Migrate Mate, which lists current openings from across the United States. Find the roles that match your background and apply directly to each listing from the page.
See All 17 Senior Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Senior Mlops Engineer Jobs