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Mlops Manager Jobs in Washington (NOW HIRING)

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on ... Managed services (e.g., SageMaker endpoints, Bedrock-style APIs) * Containerized custom inference ...

MLOps Engineer

Mclean, VA

$115K - $150K/yr

We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and ... Build and manage core ML platform components such as model registries, experiment tracking systems ...

MLOps Engineer

Mclean, VA · On-site

$115K - $150K/yr

The MLOps Engineer works closely with Data Scientists, AI Developers, Data Engineers, and cloud ... Build and manage core ML platform components such as model registries, experiment tracking systems ...

MLOps Engineer

Mclean, VA · On-site

$115K - $150K/yr

The MLOps Engineer works closely with Data Scientists, AI Developers, Data Engineers, and cloud ... Build and manage core ML platform components such as model registries, experiment tracking systems ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS ...

MLOps Platform Engineer Location: Reston VA - In person interviews so need Local In EAST coast only ... Container & Kubernetes Workloads · Design and manage EKS workloads supporting containerized ML ...

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on ... managing CI/CD pipelines (GitLab or equivalent). · Familiarity with machine learning workflows ...

Balance hands-on software development and MLOps engineering with technical delivery leadership, guiding engineers, partnering with product management, and coordinating cross-functional rollouts.

Senior MLOps Engineer

Washington, DC · On-site

$118K - $162K/yr

Lead the implementation and management of DoD responsible artificial intelligence programs and ... Establish MLOps monitoring and observability solutions for production ML systems * Develop ...

Senior MLOps Engineer

Washington, DC · On-site

$117K - $161K/yr

Lead the implementation and management of DoD responsible artificial intelligence programs and ... Establish MLOps monitoring and observability solutions for production ML systems * Develop ...

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Showing results 1-20

Mlops Manager information

What is an MLOps manager?

MLOps Managers are professionals responsible for overseeing the deployment, operation, and scaling of machine learning models in production environments. They coordinate teams to ensure seamless collaboration between data scientists, engineers, and IT staff, facilitating the automation of machine learning workflows. Their role involves managing infrastructure, optimizing processes for model monitoring and maintenance, and ensuring compliance with organizational and industry standards. MLOps Managers play a key role in bridging the gap between model development and operationalization, ensuring that machine learning solutions are reliable, reproducible, and scalable.

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

To thrive as an MLOps Manager, you need expertise in machine learning, software engineering, and DevOps practices, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and certifications such as AWS Certified Machine Learning or Google Cloud Professional ML Engineer are highly beneficial. Strong leadership, problem-solving, and cross-functional communication skills help manage teams and bridge the gap between data science and IT operations. These abilities are crucial for ensuring reliable, scalable, and efficient deployment of machine learning solutions in production environments.

What are some common challenges an MLOps manager faces when integrating machine learning models into production environments?

MLOps Managers often encounter challenges such as ensuring seamless collaboration between data science and engineering teams, managing model versioning, and maintaining reliable deployment pipelines. Balancing rapid experimentation with the need for robust, scalable, and secure production systems can be complex. Additionally, monitoring model performance post-deployment and handling data drift or model degradation are ongoing responsibilities. Effective communication and establishing standardized processes are key to overcoming these challenges and ensuring successful model operations.

What is the difference between Mlops Manager vs Data Scientist?

AspectMlops ManagerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; certifications in cloud platforms or MLOps toolsBachelor's/Master's in CS, Statistics, or related; certifications in data analysis or machine learning
Work EnvironmentCollaborates with engineering, DevOps, and data teams to deploy and maintain ML systemsAnalyzes data, builds models, and provides insights to inform business decisions
Employer & Industry UsageTech companies, AI startups, enterprises implementing ML pipelinesResearch institutions, tech firms, finance, healthcare, and marketing sectors

The Mlops Manager focuses on deploying, maintaining, and optimizing machine learning systems within an organization, working closely with engineering and DevOps teams. In contrast, a Data Scientist primarily analyzes data, develops models, and provides insights. While both roles require knowledge of machine learning, the Mlops Manager emphasizes operationalizing ML solutions, whereas the Data Scientist emphasizes data analysis and modeling.

What are the most commonly searched types of Mlops jobs in Washington?

The most popular types of Mlops jobs in Washington are:

What cities in Washington are hiring for Mlops Manager jobs?

Cities in Washington with the most Mlops Manager job openings:

MLOps Architect

Kapitus

Arlington, VA • On-site

$117K - $189K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 15 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.