Generative AI Engineer Jobs
Generative AI Engineer jobs are open across tech, financial services, healthcare, and media, from new-grad to staff and principal levels, with specializations in large language model fine-tuning, retrieval-augmented generation, and multimodal AI systems. Find a role that fits from the openings below and apply directly.
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Showing 5 of 209+ Generative AI 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.
Generative AI Engineer Jobs by Experience Level
See All 209+ Generative AI Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Generative AI Engineer JobsGenerative AI Engineer Job Market
Who's Hiring
- Innodata28I
- Google18
- Amazon Web Services11
- Citi8
- Apple8
Top Industries Hiring
- Technology & Software45
- Consulting & Professional Services12
- Investment & Asset Management11
- Accounting & Auditing7
- Electronics & Hardware6
What Employers Look For
The qualifications that appear most often in generative AI engineer jobs.
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Hands-on experience fine-tuning or prompting large language models like GPT, Llama, or Gemini
- Experience building and deploying retrieval-augmented generation pipelines
- Familiarity with model serving infrastructure including APIs, containerization, and cloud platforms
- Bachelor's or master's degree in computer science, machine learning, or a related field
- Understanding of evaluation frameworks, safety considerations, and responsible AI practices
Tips for Your Generative AI Engineer Job Search
Tailor your resume to model type
Recruiters and hiring managers scan for specific model families you've worked with. Call out LLMs, diffusion models, or multimodal architectures by name in your experience bullets, not just 'generative AI,' so your resume matches the exact language in job listings.
Show production deployments, not prototypes
Most generative AI engineer roles want evidence of systems that shipped, not Jupyter notebooks. Quantify throughput, latency improvements, or cost reductions from a deployed endpoint. Side projects count if they serve real users and you can describe the infrastructure.
Filter openings by stack before applying
Job descriptions vary widely: some teams run everything on proprietary APIs, others want low-level PyTorch experience. Read the technical requirements carefully and match your application to roles where your stack overlaps at least two thirds of what they list.
Apply early to roles that fit
Migrate Mate lists generative ai engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for system design around inference
Technical interviews at companies building generative AI products often focus on inference pipeline design: batching strategies, model serving frameworks like vLLM or TGI, and cost-per-token tradeoffs. Practicing these scenarios is more valuable than re-reading model architecture papers.
Negotiate scope before salary
Generative AI roles vary enormously in autonomy. Before discussing compensation, clarify whether you own the full ML lifecycle or support a research team. Roles with more ownership over architecture and deployment decisions typically have stronger leverage for negotiating total compensation.
Generative AI Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most generative ai engineers?
The companies hiring the most generative ai engineers right now include Innodata, Google, and Amazon Web Services, with the largest share of openings in California, Texas, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is concentrated at companies building AI-native products as well as enterprises integrating generative AI into existing platforms.
How many generative ai engineer jobs are remote?
About 68% of generative ai engineer openings are fully remote or hybrid as of August 2026, making it one of the more flexible engineering disciplines. Roles focused on prompt engineering, API integration, and evaluation tend to be most commonly offered remotely, while positions involving proprietary infrastructure or on-premise model deployment are more likely to require on-site presence.
How do you become a generative ai engineer?
Start by building a strong foundation in Python and machine learning fundamentals, then move into hands-on work with transformer architectures and pre-trained models through open-source projects or coursework. Practice fine-tuning models on domain-specific datasets, build at least one end-to-end application that uses a generative model in production, and document the infrastructure decisions you made along the way. Familiarity with vector databases, prompt engineering patterns, and model evaluation methods rounds out the core skill set employers look for.
Can you get a generative ai engineer job with little experience?
Yes, entry-level generative AI roles exist, and employers hiring at that level prioritize demonstrated projects over years of experience. Build a public portfolio that shows you've integrated an LLM into a real application, contributed to an open-source AI project, or fine-tuned a model on a specific dataset. Roles titled AI engineer, ML engineer, or applied AI developer often have lower experience bars and serve as strong entry points into the field.
What does the generative ai engineer interview process look like?
Most generative AI engineer interviews include a recruiter screen followed by a technical phone interview covering Python, ML concepts, and prior project experience. A take-home or live coding assessment typically tests your ability to build or evaluate a generative AI component, such as a retrieval pipeline or an evaluated prompt chain. Final rounds usually include a system design interview focused on inference infrastructure and a cross-functional conversation with product or research stakeholders about your approach to model tradeoffs and safety.
Where can I find and apply to generative ai engineer jobs?
You can find and apply to generative ai engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience and specialization, then apply directly to each one that fits. The platform pulls in openings from a wide range of companies, so you can compare roles across industries without searching multiple sites.
See All 209+ Generative AI Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Generative AI Engineer Jobs