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Remote Risk Engineer Jobs in Virginia Beach, VA (NOW HIRING)

Excellent communication skills-able to translate technical risk into business impact for non ... This is a remote position. While performing the duties of this job, the employee regularly works in ...

Senior Project Controls Engineer

Newport News, VA · Remote

$135K - $165K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... Risk Register, Change Request Process, Contract budget base log, Earned Value Management System ... LI-REMOTE Qualifications: * Bachelor's degree. * A minimum of twelve (12) years of experience in ...

New

Work with engineering leads, product managers, and domain experts to translate business ... Know when an estimate doesn't add up, when a design needs more thought, and when risk is being ...

Program Manager I

Chesapeake, VA · On-site +1

$135K - $170K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Taxable Entity ALUTIIQ SOLUTIONS LLC Job Title Program Manager I Location VA Remote - Remote, VA ... Possess a Bachelor's degree in a relevant field such as project management, engineering ...

Navy Client Account Manager

Norfolk, VA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... engineers, scientists, digital innovators, program and construction managers and other ... This position will offer flexibility for primarily remote work schedules and can be based from a ...

Navy Client Account Manager

Norfolk, VA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... engineers, scientists, digital innovators, program and construction managers and other ... This position will offer flexibility for primarily remote work schedules and can be based from a ...

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Showing results 1-20

Remote Risk Engineer information

See Virginia Beach, VA salary details

$36.1K

$110K

$181.7K

How much do remote risk engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for remote risk engineer in Virginia Beach, VA is $109,958.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,800.00 and $143,800.00 per year, depending on experience, location, and employer.

How does a remote risk engineer typically collaborate with cross-functional teams to address potential risks?

As a Remote Risk Engineer, you’ll regularly work with cross-functional teams such as IT, operations, product management, and compliance to identify, assess, and mitigate potential risks. Collaboration often involves virtual meetings, shared documentation, and real-time communication tools to ensure risks are clearly communicated and addressed promptly. You may participate in risk assessments, review system designs, and provide recommendations to enhance security and operational resilience. Building strong relationships and maintaining proactive communication with team members is key to ensuring risks are managed effectively, even when working remotely.

What are the key skills and qualifications needed to thrive as a remote risk engineer?

To thrive as a Remote Risk Engineer, you need a solid background in risk assessment, engineering principles, and relevant degree qualifications such as in engineering or risk management. Familiarity with risk modeling software, data analysis tools, and industry-specific certifications like Certified Risk Engineer (CRE) are often required. Strong analytical thinking, problem-solving, and effective virtual communication skills help you excel when collaborating with clients and remote teams. These capabilities are crucial to accurately identifying risks, designing mitigation strategies, and ensuring the safety and compliance of client operations from a remote environment.

What is the difference between Remote Risk Engineer vs Remote Underwriter?

AspectRemote Risk EngineerRemote Underwriter
Required CredentialsBachelor's in Engineering, Risk Management certificationsBachelor's in Finance, Insurance certifications
Work EnvironmentAnalyzing technical risks, data modelingAssessing insurance applications, policy evaluation
Employer & Industry UsageInsurance, finance, engineering firmsInsurance companies, brokerage firms

Remote Risk Engineers focus on analyzing technical and operational risks using engineering principles, while Remote Underwriters evaluate insurance applications and determine policy terms. Both roles require analytical skills and industry-specific certifications, often working remotely for insurance or risk management companies. Understanding these differences helps job seekers identify the right career path based on their skills and interests.

What is a remote risk engineer?

A Remote Risk Engineer is a professional who evaluates and manages risks for an organization, often related to safety, cybersecurity, or insurance, while working from a remote location. They analyze data, identify potential hazards or vulnerabilities, and recommend solutions to minimize losses or damages. This job typically requires strong analytical skills, knowledge of risk assessment methodologies, and effective communication, as much of the work may involve collaborating virtually with teams and clients. Remote Risk Engineers may specialize in various industries, including finance, manufacturing, or technology.

What are popular job titles related to Remote Risk Engineer jobs in Virginia Beach, VA?

For Remote Risk Engineer jobs in Virginia Beach, VA, the most frequently searched job titles are:

What job categories do people searching Remote Risk Engineer jobs in Virginia Beach, VA look for?

The top searched job categories for Remote Risk Engineer jobs in Virginia Beach, VA are:

What cities near Virginia Beach, VA are hiring for Remote Risk Engineer jobs?

Cities near Virginia Beach, VA with the most Remote Risk Engineer job openings:

Infographic showing various Remote Risk Engineer job openings in Virginia Beach, VA as of August 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 100% Remote job distribution, with an average salary of $109,958 per year, or $52.9 per hour.

Senior MLOps & Generative AI Engineer - Remote

Sentara Health

Virginia Beach, VA • On-site, Remote

$90K - $123K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

This job post has expired today. Applications are no longer accepted.


Sentara Health rating

6.7

Company rating: 6.7 out of 10

Based on 412 frontline employees who took The Breakroom Quiz

531st of 887 rated healthcare providers


Job description

City/State
Virginia Beach, VA
Work Shift
Multiple shifts available
Overview:
Sentara is hiring a Senior MLOps & Generative AI Engineer!
This position is fully remote!
Selected candidates would be required to be onsite for final round of team interview
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
Benefits: Caring For Your Family and Your Career
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
•Pet Insurance
•Legal Resources Plan
•Colleagues have the opportunity to earn an annual discretionary bonus ifestablished 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.

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