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Additive Manufacturing Engineer Remote Jobs in Virginia

QCi

Senior Program Manager - Manufacturing Operations Location: Tempe, AZ or Remote Division ... Automate the handoff of code releases from development to operations (DevOps). * Participate in the ...

Solenis

Sales Support Engineer (Process Engineer)

Richmond, VA · On-site +1

$77K - $113K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

... functional additives, cleaners, disinfectants, and state-of-the-art monitoring, control and ... Headquartered in Wilmington, Delaware, the company has 78 manufacturing facilities strategically ...

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Additive Manufacturing Engineer Remote information

What are the key skills and qualifications needed to thrive as an additive manufacturing engineer remote?

To thrive as an Additive Manufacturing Engineer (Remote), you need a strong background in engineering principles, 3D printing technologies, and materials science, usually supported by a relevant engineering degree. Proficiency with CAD software, simulation tools, and experience with additive manufacturing systems such as FDM, SLS, or SLA, as well as familiarity with industry standards or certifications like ASTM or ISO, is essential. Strong problem-solving skills, attention to detail, and effective virtual communication are crucial soft skills for remote collaboration and project management. These competencies ensure the engineer can design, optimize, and deliver high-quality parts while collaborating efficiently with distributed teams.

What does an additive manufacturing engineer do when working remotely?

An Additive Manufacturing Engineer working remotely is responsible for designing, developing, and optimizing 3D printing processes and products from a remote location. They collaborate with teams using digital tools to create prototypes, analyze data, and troubleshoot issues related to additive manufacturing. Remote engineers may also oversee workflow, ensure quality standards are met, and support the integration of new technologies, all while communicating with clients and colleagues virtually.

What is the difference between Additive Manufacturing Engineer Remote vs Additive Manufacturing Technician?

AspectAdditive Manufacturing Engineer RemoteAdditive Manufacturing Technician
CredentialsBachelor's or higher in engineering, certifications like AM-specific trainingTechnical diploma or associate degree, hands-on training
Work EnvironmentRemote office, design and development focusOn-site manufacturing floor, equipment operation
Industry UsageDesign, process optimization, project managementMachine setup, maintenance, part fabrication

The main difference is that Additive Manufacturing Engineers Remote focus on designing and optimizing additive processes remotely, while Additive Manufacturing Technicians work hands-on with equipment on-site. Engineers typically require higher education and perform planning tasks, whereas Technicians handle daily machine operations and maintenance.

What are some common challenges faced by remote additive manufacturing engineers, and how can they be addressed?

Remote Additive Manufacturing Engineers often encounter challenges related to limited hands-on access to equipment and real-time collaboration with on-site teams. To overcome these obstacles, it's important to establish clear communication channels, utilize remote monitoring tools, and coordinate closely with local operators for prototyping and testing. Regular virtual meetings and detailed documentation help ensure project timelines stay on track and technical issues are resolved efficiently. Embracing digital collaboration platforms and maintaining flexibility can significantly enhance productivity and team integration.

What are the most commonly searched types of Additive Manufacturing Engineer jobs in Virginia?

The most popular types of Additive Manufacturing Engineer jobs in Virginia are:

What are popular job titles related to Additive Manufacturing Engineer Remote jobs in Virginia?

For Additive Manufacturing Engineer Remote jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Additive Manufacturing Engineer Remote jobs in Virginia look for?

The top searched job categories for Additive Manufacturing Engineer Remote jobs in Virginia are:

What cities in Virginia are hiring for Additive Manufacturing Engineer Remote jobs?

Cities in Virginia with the most Additive Manufacturing Engineer Remote job openings:

Additive Manufacturing Engineer Remote jobs near you
Infographic showing various Additive Manufacturing Engineer Remote job openings in Virginia as of August 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 100% Remote job distribution.

Senior MLOps & Generative AI Engineer - Remote

Sentara Healthcare

Virginia Beach, VA • On-site, Remote

$99K - $136K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago

Sentara Health rating

6.7

Company rating: 6.7 out of 10

Based on 412 frontline employees who took The Breakroom Quiz

530th 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 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.

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