Mlops Engineer Jobs
Mlops Engineer jobs are open across tech, finance, healthcare, and e-commerce, from junior to staff and principal level, with specializations in model deployment, pipeline automation, and infrastructure reliability. Find a role that fits from the openings below and apply directly.
Find Mlops Engineer JobsLooking for remote work? View remote mlops engineer jobs →Overview
Showing 5 of 42+ Mlops Engineer jobs
City/State
Virginia Beach, VAWork Shift
Multiple shifts availableOverview:
Sentara is hiring a Senior MLOps & Generative AI Engineer!
This position is fully remote!
Candidates must reside in one of the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, or Wyoming.
Overview
We are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence.
This role combines two critical focus areas:
MLOps Engineering — building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.
Generative AI Engineering — designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.
As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization’s AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments.
Key Responsibilities
MLOps Engineering Responsibilities
Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
Support enterprise AI governance, compliance, auditability, and model risk management requirements.
Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
Generative AI Engineering Responsibilities
Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.
Required Qualifications
5+ years of experience building and deploying production software, ML systems, or AI platforms.
1+ years of hands-on experience building production Generative AI or LLM-based applications.
Strong programming skills in Python and experience with software engineering best practices.
Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
Experience deploying AI/ML systems in cloud environments including AWS, Azure, or GCP.
Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures.
Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure.
Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
Experience building monitoring, observability, and alerting solutions for ML and AI systems.
Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
Experience designing secure, scalable, production-ready AI platforms and services.
Strong communication and collaboration skills with the ability to work across technical and business teams.
Preferred Qualifications
Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
Experience working with EPIC or healthcare interoperability platforms.
Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
Experience with GPU infrastructure optimization and scalable inference architectures.
Familiarity with multi-agent AI systems and autonomous workflows.
Experience with event-driven architectures, streaming pipelines, and real-time inference systems.
Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
Experience with enterprise AI platform architecture and internal developer platforms.
Prior experience mentoring engineers and leading technical initiatives.
Education
5+ years of relevant experience with a degree (Required)
or
7+ years of relevant experience without a degree (Required)
Experience in lieu of Bachelor’s Degree.
Certification/Licensure
No specific certification or licensure requirements
Experience
5 to 7 years of relevant experience
We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for Full Time employment is: $91,416.00 - $152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities.
Keywords: Talroo-IT, MLOps, Gen AI, LLM, AWS, Azure, GCP, AI/ML, Python, PyTorch, Hugging Face Transformers, TensorFlow, RAG, EPIC, HIPAA, AI Governance
- Medical, Dental, Vision plans
- Adoption, Fertility and Surrogacy Reimbursement up to $10,000
- Paid Time Off and Sick Leave
- Paid Parental & Family Caregiver Leave
- Emergency Backup Care
- Long-Term, Short-Term Disability, and Critical Illness plans
- Life Insurance
- 401k/403B with Employer Match
- Tuition Assistance – $5,250/year and discounted educational opportunities through Guild Education
- Student Debt Pay Down – $10,000
- Reimbursement for certifications and free access to complete CEUs and professional development
- Pet Insurance
- Legal Resources Plan
- Colleagues have the opportunity to earn an annual discretionary bonus if established system and employee eligibility criteria is met.
Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves.
In support of our mission “to improve health every day,” this is a tobacco-free environment.
For positions that are available as remote work, Sentara Health employs associates in the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.
City/State
Virginia Beach, VAWork Shift
Multiple shifts availableOverview:
Sentara is hiring a Senior MLOps & Generative AI Engineer!
This position is fully remote!
Candidates must reside in one of the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, or Wyoming.
Overview
We are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence.
This role combines two critical focus areas:
MLOps Engineering — building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.
Generative AI Engineering — designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.
As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization’s AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments.
Key Responsibilities
MLOps Engineering Responsibilities
Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
Support enterprise AI governance, compliance, auditability, and model risk management requirements.
Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
Generative AI Engineering Responsibilities
Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.
Required Qualifications
5+ years of experience building and deploying production software, ML systems, or AI platforms.
1+ years of hands-on experience building production Generative AI or LLM-based applications.
Strong programming skills in Python and experience with software engineering best practices.
Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
Experience deploying AI/ML systems in cloud environments including AWS, Azure, or GCP.
Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures.
Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure.
Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
Experience building monitoring, observability, and alerting solutions for ML and AI systems.
Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
Experience designing secure, scalable, production-ready AI platforms and services.
Strong communication and collaboration skills with the ability to work across technical and business teams.
Preferred Qualifications
Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
Experience working with EPIC or healthcare interoperability platforms.
Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
Experience with GPU infrastructure optimization and scalable inference architectures.
Familiarity with multi-agent AI systems and autonomous workflows.
Experience with event-driven architectures, streaming pipelines, and real-time inference systems.
Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
Experience with enterprise AI platform architecture and internal developer platforms.
Prior experience mentoring engineers and leading technical initiatives.
Education
5+ years of relevant experience with a degree (Required)
or
7+ years of relevant experience without a degree (Required)
Experience in lieu of Bachelor’s Degree.
Certification/Licensure
No specific certification or licensure requirements
Experience
5 to 7 years of relevant experience
We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for Full Time employment is: $91,416.00 - $152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities.
Keywords: Talroo-IT, MLOps, Gen AI, LLM, AWS, Azure, GCP, AI/ML, Python, PyTorch, Hugging Face Transformers, TensorFlow, RAG, EPIC, HIPAA, AI Governance
- Medical, Dental, Vision plans
- Adoption, Fertility and Surrogacy Reimbursement up to $10,000
- Paid Time Off and Sick Leave
- Paid Parental & Family Caregiver Leave
- Emergency Backup Care
- Long-Term, Short-Term Disability, and Critical Illness plans
- Life Insurance
- 401k/403B with Employer Match
- Tuition Assistance – $5,250/year and discounted educational opportunities through Guild Education
- Student Debt Pay Down – $10,000
- Reimbursement for certifications and free access to complete CEUs and professional development
- Pet Insurance
- Legal Resources Plan
- Colleagues have the opportunity to earn an annual discretionary bonus if established system and employee eligibility criteria is met.
Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves.
In support of our mission “to improve health every day,” this is a tobacco-free environment.
For positions that are available as remote work, Sentara Health employs associates in the following states:
Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.
Mlops Engineer Jobs by Experience Level
See All 42 Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Mlops Engineer JobsMlops Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software10
- Retail1
- Fintech1
- Education1
- Automotive1
What Employers Look For
The qualifications that appear most often in mlops engineer jobs.
- Proficiency in Python and experience building and maintaining ML pipelines at scale
- Hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
- Familiarity with at least one major cloud platform including AWS, GCP, or Azure ML services
- Experience with ML experiment tracking and model registry tools such as MLflow or Weights and Biases
- Understanding of CI/CD principles and tooling applied to machine learning model deployment workflows
- Bachelor's degree in computer science, data engineering, or a closely related technical field
Tips for Your Mlops Engineer Job Search
Tailor your resume to deployment pipelines
Hiring managers scan for specific orchestration tools like Kubeflow, MLflow, or Airflow. List each tool with the scale you operated at and the problem it solved, not just the name. Vague 'used ML tools' lines get skipped.
Apply early to roles that fit
Migrate Mate lists mlops engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Highlight model monitoring and observability work
Many mlops engineer candidates oversell training pipelines and undersell production monitoring. Emphasize experience with drift detection, alerting, and retraining triggers because those are the gaps most teams are actually trying to fill.
Filter openings by cloud platform overlap
AWS, GCP, and Azure mlops tooling diverge sharply. When targeting roles, prioritize postings that name the cloud stack you know deepest so your hands-on experience answers the interview's first technical question before you walk in.
Prepare a live demo of a deployed model endpoint
Interviewers for mlops roles frequently ask you to walk through a real system you built. Having a publicly accessible endpoint or a recorded walkthrough of a CI/CD model pipeline makes that conversation concrete and memorable.
Negotiate scope before you negotiate salary
In mlops offers, clarify whether the role owns infrastructure decisions or only executes on them. A title of 'mlops engineer' can mean staff-level architecture ownership or pure tooling maintenance, and that distinction shapes long-term growth more than base pay.
Mlops Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most mlops engineers?
The companies hiring the most mlops engineers right now include GRVTY, TRM Labs, and Apple, with the largest share of openings in California, Hawaii, and Ohio, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in organizations scaling production ML systems beyond the experimentation phase.
How many mlops engineer jobs are remote?
About 54% of mlops engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible infrastructure roles in tech. Model deployment, pipeline development, and monitoring work tend to be the most remote-friendly sub-areas, while roles requiring close collaboration with on-prem GPU clusters more often require on-site presence.
How do you become a mlops engineer?
Start by building a strong foundation in software engineering and data engineering before layering on ML system knowledge. Learn to containerize models with Docker, automate deployments with a CI/CD tool, and instrument a live model endpoint with monitoring. Contributing to open-source mlops tooling or publishing a documented end-to-end pipeline project accelerates hiring conversations significantly.
Can you get hired as a mlops engineer with little experience?
Yes, but you need a portfolio that proves production thinking, not just experimentation. Build and document a complete pipeline that trains, versions, deploys, and monitors a model in a cloud environment. Roles titled 'associate mlops engineer' or 'mlops platform engineer' at growth-stage companies often hire candidates who show systems thinking even without years of prior mlops-specific experience.
What does the mlops engineer interview process look like?
Most mlops engineer interview processes include a recruiter screen, a technical phone interview covering Python and infrastructure concepts, a take-home or live system design exercise focused on a deployment or pipeline problem, and a final round with engineering and data science stakeholders. Expect at least one question asking you to debug or improve an existing pipeline rather than build from scratch.
Where can I find and apply to mlops engineer jobs?
You can find and apply to mlops engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and tools, then apply directly to each listing. New openings are added regularly, so checking back frequently helps you catch roles before they close.
See All 42 Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Mlops Engineer Jobs