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Mlops Engineer Remote Jobs in Reston, VA (NOW HIRING)

DevOps/MLOps Engineer

Ashburn, VA ยท On-site +1

$54 - $74/hr

Remote Work: Niyam understands the value of flexibility. We offer remote work. * Career Growth ... MLOps Engineer to join our team in support of our work with a federal client.This role is ...

Machine Learning Engineer - Remote

Vienna, VA ยท On-site +1

$140K - $150K/yr

Deployment & MLOps * Operationalize models with robust CI/CD workflows. * Deploy models usingMLflow ... Engineer high-quality features and maintain training/inference pipelines. Cloud and Platform ...

Machine Learning & Operations Engineer

Arlington, VA ยท Remote

$80K - $108K/yr

... MLOps system and provide other support to teams working on projects involving machine learning ... This is a fully remote position, working cross-functionally with research and engineering teams.

Machine Learning & Operations Engineer

Arlington, VA ยท Remote

$80K - $108K/yr

... MLOps system and provide other support to teams working on projects involving machine learning ... This is a fully remote position, working cross-functionally with research and engineering teams.

Machine Learning & Operations Engineer

Arlington, VA ยท Remote

$71K - $96K/yr

... MLOps system and provide other support to teams working on projects involving machine learning ... This is a fully remote position, working cross-functionally with research and engineering teams.

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Mlops Engineer Remote information

See Reston, VA salary details

$39.5K

$120.5K

$199.2K

How much do mlops engineer remote jobs pay per year?

As of Jun 11, 2026, the average yearly pay for mlops engineer remote in Reston, VA is $120,540.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,300.00 and $157,600.00 per year, depending on experience, location, and employer.

What are some common challenges faced by remote MLOps Engineers, and how can they be addressed?

Remote MLOps Engineers often encounter challenges related to communication and collaboration, especially when coordinating with data scientists, developers, and operations teams across different time zones. To overcome these challenges, it's essential to establish clear documentation practices, utilize collaborative platforms for workflow management, and schedule regular virtual meetings to ensure alignment. Additionally, maintaining strong version control and automated CI/CD pipelines helps streamline model deployment and monitoring, reducing friction caused by remote coordination. Building proactive communication habits and leveraging cloud-based tools can significantly improve efficiency and team cohesion.

What is the difference between Mlops Engineer Remote vs Data Engineer?

AspectMlops Engineer RemoteData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; experience with cloud platforms and ML toolsBachelor's in CS, Data Engineering, or related; strong SQL and ETL skills
Work EnvironmentRemote, collaborative teams, cloud-based infrastructureRemote or on-site, data pipelines, cloud or on-premises systems
Industry UsageTech, AI, ML-focused companiesFinance, healthcare, tech, and other data-driven industries

While both roles involve working with data and cloud platforms, Mlops Engineers focus on deploying and maintaining machine learning models in production, often working remotely with ML-specific tools. Data Engineers primarily build and manage data pipelines and infrastructure. The roles overlap in cloud experience and data handling but differ in their core focus areas.

What does an MLOps Engineer do, especially in a remote role?

An MLOps Engineer is responsible for streamlining and automating the deployment, monitoring, and management of machine learning models in production environments. Working remotely, they collaborate with data scientists, software engineers, and IT teams using cloud-based tools to ensure that ML models are scalable, reliable, and maintainable. Their tasks often include setting up CI/CD pipelines for ML workflows, managing model versioning, and monitoring model performance over time. Remote MLOps Engineers leverage communication and project management tools to stay aligned with distributed teams and ensure seamless operations.

What are the key skills and qualifications needed to thrive as an MLOps Engineer (Remote), and why are they important?

To thrive as an MLOps Engineer, you need a solid background in machine learning, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Azure, as well as certifications in cloud services or DevOps, are highly valuable. Strong problem-solving, collaboration, and communication skills help you bridge the gap between data science and operations teams in a remote setting. These competencies are crucial for building scalable, reliable machine learning systems that deliver real-world value efficiently.
What are the most commonly searched types of Mlops Engineer jobs in Reston, VA? The most popular types of Mlops Engineer jobs in Reston, VA are:
What are popular job titles related to Mlops Engineer Remote jobs in Reston, VA? For Mlops Engineer Remote jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Mlops Engineer Remote jobs in Reston, VA look for? The top searched job categories for Mlops Engineer Remote jobs in Reston, VA are:
What cities near Reston, VA are hiring for Mlops Engineer Remote jobs? Cities near Reston, VA with the most Mlops Engineer Remote job openings:
Principal MLOps Engineer with Security Clearance

Principal MLOps Engineer with Security Clearance

Raft

Mclean, VA โ€ข Remote

Other

Medical, Dental, Vision, Retirement, PTO

Posted 25 days ago


Job description

Principal MLOps Engineer Remote, US; DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S. Who we are: Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans. Weโ€™re looking for an experienced Principal ML Opsย Engineer to support our customers and join our passionate team of high-impact problem solvers. About the role: Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making. Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments. In this role, you will help design, deploy, and mature Raftโ€™s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models. What youโ€™ll do: Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems Help mature Raftโ€™s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems Build and maintain CI/CD workflows for ML services, model artifacts, and platform components Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment Improve observability, reliability, security, and maintainability across ML infrastructure and services Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments Support compliance-driven deployment practices and secure software supply chain requirements in defense environments Get hands-on with customers at the most forward-leaning places in the Department of War What we are looking for: 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles 5+ years of experience with Docker and Kubernetes in production environments 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams Practical experience deploying machine learning workloads on Kubernetes Experience managing clusters or workloads that use GPUs Strong understanding of Helm and Kubernetes deployment patterns Strong scripting or programming skills, preferably in Python Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows Strong troubleshooting, systems thinking, and communication skills Ability to work independently and collaboratively in a fast-moving environment Ability to obtain and maintain a Top Secret clearance Ability to obtain Security+ certification within the first 90 days of employment Highly preferred: Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies Experience with secure and compliant deployment practices in regulated or government environments Experience with Kubernetes-based ML platforms such as Kubeflow Familiarity with service mesh technologies such as Istio Experience provisioning and debugging complex CI/CD systems Experience with infrastructure as code tools such as Terraform Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge Background working with machine learning engineers on model training, evaluation, packaging, and release workflows Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system What success looks like: You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use Model deployments become more secure, observable, and supportable across real-world mission environments The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership Clearance Requirements: Ability to obtain and maintain a Top Secret clearanceย  Work Type:ย  Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY May require up to 40% travel Salary Range: $150,000.00 - $200,000.00 What we will offer you: Highly competitive salary Fully covered healthcare, dental, and vision coverage 401(k) and company match Take as you need PTO + 11 paid holidays Education & training benefits Annual budget for your tech/gadgets needs Monthly box of yummy snacks to eat while doing meaningful work Remote, hybrid, and flexible work options Team off-site in fun places! Generous Referral Bonuses * And More! Our Vision Statement:ย  We bridge the gap between humans and data through radical transparency and our obsession withย theย mission.ย  Our Customer Obsession:ย  We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies. How do we get there?ย  Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking,ย and collaboration is a norm.ย  Raftโ€™s core philosophy isย Ubuntu: Iย Am, Becauseย We are. We support ourย โ€œnadiโ€ย by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.ย  Weโ€™re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Create a Job Alert Interested in building your career at Raft Company Website? Get future opportunities sent straight to your email.
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