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.
Find Senior Mlops Engineer JobsOverview
Showing 5 of 13+ Senior 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.
Senior Mlops Engineer Jobs by Experience Level
See All 13 Senior Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Senior Mlops Engineer JobsSenior Mlops Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Automotive
- Artificial Intelligence
- Healthcare & Medical Services
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 Forward Financing, with the largest share of openings in California, Massachusetts, and Oregon, based on current listings on Migrate Mate as of August 2026. Demand is especially concentrated at companies running large-scale model inference in production.
How many senior mlops engineer jobs are remote?
About 70% of senior mlops engineer openings are fully remote or hybrid as of August 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 13 Senior Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Senior Mlops Engineer Jobs