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Mlops Jobs in Virginia (NOW HIRING)

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch). * Architect end-to-end ML and ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

They are seeking an experienced MLOps Engineer to join their Data and AI team, focusing on developing robust data solutions to support Machine Learning, Data Science, and Software Engineering ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality, innovative projects. Our team integrates cutting-edge technologies into the construction process to ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality, innovative projects. Our team integrates cutting-edge technologies into the construction process to ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

MLOps Platform Engineer Location: Reston VA - In person interviews so need Local In EAST coast only Description: MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking ...

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, CloudWatch, and ECR. · Strong experience ...

MLOps Platform Engineer Location: Reston VA Required Qualifications • 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, CloudWatch, and ECR. • Strong experience ...

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Showing results 1-20

Mlops information

See Virginia salary details

$98.7K

$154.9K

$184.3K

How much do mlops jobs pay per year?

As of Aug 7, 2026, the average yearly pay for mlops in Virginia is $154,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $146,365.00 and $168,247.00 per year, depending on experience, location, and employer.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are the most commonly searched types of Mlops jobs in Virginia? The most popular types of Mlops jobs in Virginia are:
What cities in Virginia are hiring for Mlops jobs? Cities in Virginia with the most Mlops job openings:
Infographic showing various Mlops job openings in Virginia as of August 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 92% In-person, and 8% Hybrid job distribution, with an average salary of $154,911 per year, or $74.5 per hour.

MLOps Architect

Kapitus

Arlington, VA • On-site

$117K - $189K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago


Job description

We are seeking a senior MLOps Architect to design and scale a modern ML and Generative AI platform across AWS. This role will own the architecture for traditional ML and LLM/Generative AI pipelines, ensuring production reliability, governance, cost optimization (FinOps), and enterprise-grade security. The ideal candidate has deep expertise in AWS, SageMaker, Databricks, Atlan (data catalog/governance), and modern MLOps tooling, and understands how to operationalize LLMs, RAG systems, and foundation models within a governed, scalable MLOps stack. This is a strategic, hands-on architecture role responsible for integrating GenAI capabilities into an enterprise ML platform.

What you’ll Do:

MLOps & GenAI Platform Architecture

  • Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch).
  • Architect end-to-end ML and Generative AI lifecycle workflows:
    • Data ingestion & preprocessing o Feature engineering / embedding generation o Model training & fine-tuning (traditional ML + foundation models)
    • Model evaluation & validation
    • Deployment (real-time, batch, streaming)
    • Monitoring & retraining
  • Integrate LLM pipelines (prompt workflows, RAG architectures, fine-tuning flows) into the enterprise MLOps stack.
  • Define standards for CI/CD/CT pipelines across ML and GenAI workloads.

Generative AI & LLM Operationalization

  • Architect Retrieval-Augmented Generation (RAG) pipelines including:
    • Embedding generation workflows
    • Vector database integration
    • Document ingestion and chunking strategies
    • Retrieval evaluation and monitoring
  • Design and deploy LLM-based services using:
    • Managed services (e.g., SageMaker endpoints, Bedrock-style APIs)
    • Containerized custom inference services
  • Establish prompt versioning, evaluation frameworks, and experiment tracking for LLM systems.
  • Implement guardrails for hallucination control, safety monitoring, bias detection, and usage logging.
  • Define architecture for LLM fine-tuning workflows (including data curation, evaluation, and cost controls).
  • Implement scalable orchestration of LLM pipelines using workflow engines and event-driven patterns.

Deployment, Monitoring & Reliability

  • Architect scalable inference patterns for:
    • Traditional ML models
    • LLM APIs
    • RAG systems
  • Implement model monitoring frameworks for:
    • Performance degradation
    • Drift detection
    • LLM output quality
    • Latency and token usage metrics
  • Define SLAs/SLOs for ML and GenAI systems.
  • Design safe deployment strategies (blue/green, canary, shadow testing).
  • Establish logging, observability, and traceability standards for GenAI systems

 

FinOps & Cost Optimization

  • Implement cost tracking for:
    • Training workloads o GPU utilization
    • Inference endpoints o Token consumption (LLM APIs)
    • Vector database storage
  • Optimize LLM workloads for cost-performance tradeoffs (model size, batching, caching strategies).
  • Design autoscaling and compute optimization strategies for GPU and CPU-based inference.
  • Partner with finance and engineering teams to forecast ML/GenAI infrastructure spend.

Platform Enablement & Standards

  • Define enterprise standards for:
    • Experiment tracking
    • Model registry
    • Prompt registry
    • Artifact management
    • Embedding versioning
  • Provide architectural guidance to data science, AI, and engineering teams.
  • Evaluate and recommend tooling across the ML/GenAI stack (MLflow, feature    stores, vector databases, orchestration tools).
  • Drive documentation and reusable patterns for ML and GenAI development.

What We’re Looking for

 

  • 6+ years of experience in ML engineering, data engineering, or MLOps roles.
  • Proven experience architecting ML platforms in AWS.
  • Strong hands-on experience with SageMaker (training, pipelines, deployment).
  • Experience operationalizing LLM or Generative AI systems in production.
  • Experience building RAG pipelines and integrating vector databases.
  • Experience working with Databricks in production.
  • Experience implementing data governance and catalog systems (e.g., Atlan).
  • Strong understanding of CI/CD principles for ML and GenAI.
  • Experience with containerization (Docker) and orchestration (Kubernetes/EKS).
  • Deep knowledge of infrastructure-as-code (Terraform, CloudFormation).
  • Strong understanding of observability and monitoring for ML systems.
  • Experience implementing cloud cost optimization strategies (FinOps).
  • Strong Python proficiency.
  • Experience with foundation model fine-tuning and parameter-efficient methods.
  • Experience implementing model registries and experiment tracking tools.
  • Experience designing feature stores and embedding stores.
  • Familiarity with AI risk management, bias mitigation, and safety controls.
  • Experience supporting regulated or data-sensitive environments.
  • Platform-level architectural thinking.
  • Deep understanding of how to integrate GenAI into enterprise ML ecosystems.
  • Ability to balance scalability, governance, security, performance, and cost.
  • Strong technical leadership and cross-functional collaboration skills.
  • Hands-on ability to move from architecture design to implementation

Kapitus Total Rewards Package Includes: 

  • Competitive Base Salary Range of $117,800 – $189,000 Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant’s final salary will depend on a number of factors including the applicant’s geographic location, skills, and experience.
  • Annual Incentive Compensation Eligibility  Up to 10% annually
  • Health Insurance: Comprehensive medical, dental, and employer-paid vision plans through UnitedHealthcare (UHC), with various coverage levels available to meet the needs of our employees and their families. Additional perks through UHC include: Sweat Equity, free subscription to the Calm App, UHC rewards, Real Appeal, and Quit For Life.
  • Flexible Spending Account: Set aside pre-tax dollars from your paycheck to pay for qualified out-of-pocket medical, dental, vision, pharmacy or dependent care expenses. 
  • Lifestyle Spending Account: Employer sponsored post-tax benefits that allow reimbursement for expenses related to physical, mental and financial well-being. 
  • 100% Company Paid Insurances: Kapitus fully covers the cost of basic short-term and long-term disability insurance, as well as vision insurance, ensuring our employees have comprehensive protection without any personal expense.
  • Voluntary Insurance: Supplemental life insurance as well as enhanced short- and long-term disability coverage are available through Mutual of Omaha, providing additional security for our employees. Additionally, Colonial Accident and Hospitalization insurances are also available, offering further protection against unforeseen events.
  • Paid Maternity and Parental Leave: Beyond state-mandated leave policies, Kapitus provides company-paid maternity and parental leave, supporting our employees during important family milestones.
  • Commuter Benefits: We offer pre-tax benefits on parking and commuter expenses to cover travel to and from work.
  • LifeBalance Program: Enhance your lifestyle with our LifeBalance membership, which offers discounts on outdoor activities, the arts, health, and fitness. Additional benefits include: 
    • Pet and car insurance discounts.
    • Financial services such as LegalShield.
    • Relaxation and stress management tools.
  • Plum Benefits Discount Program: Access exclusive discounts on shows, travel, car rentals, and more, enriching your personal and family life.
  • Tuition Reimbursement: Pursue further education with up to $5,000 annually in tuition reimbursement, plus opportunities to attend relevant conferences and career development events. Managed through our LSA plan, Kapitus Academy. 
  • Travel Reimbursement: We also offer travel reimbursement for all work-related travel, supporting your involvement in career and personal development activities.
  • Paid Time Off and Sick Time.
  • Retirement Benefits: Our 401K plan is managed through Fidelity. To support your long-term financial goals, the company provides a 25% match on your contributions, up to 6% of your annual salary.

About Kapitus:

Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed. We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work. We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.

Company Mission: At Kapitus, our mission is to help small business owners grow their organizations by providing tailored, transparent, and ethical financing solutions. We invest in every business owner’s story and we are dedicated to building lasting relationships to champion their goals. We promise to keep the best interests of our clients at the center of the financing process by operating with transparency, fairness, and integrity.

Consideration will be given to qualified remote candidates residing in states where Kapitus and/or one of its subsidiaries has an established physical presence.

Company Description

Kapitus is one of the most reliable and respected names in small business financing. As both a direct lender and a marketplace built with a trusted network of lending partners, we can provide small businesses with the financing they need when, and how it is needed.

We have spent our entire existence building a culture that makes us excited to come to work in the morning. Our company is fast paced, teammates need to be self-directed and have an internal motivation to do the right thing, even when the right thing takes a lot of hard work.
We show our teammates our appreciation by offering great benefits, competitive pay and solid opportunity for growth.