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Mlops Machine Learning Engineer Jobs in Washington, DC

Machine Learning Engineer

Reston, VA ยท On-site

$110 - $170/hr

Machine Learning Engineer LOCATION Reston, VA 20190 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and innovative Machine ...

Machine Learning Engineer LOCATION Chantilly, VA 20151 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and innovative ...

MLOps Engineer

Mclean, VA

$115K - $150K/yr

We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client engagements.

Machine Learning Engineer LOCATION Reston, VA 20190 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and innovative Machine ...

Machine Learning Engineer LOCATION Tysons, VA 22182 CLEARANCE TS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARY We are seeking a talented and innovative Machine ...

Machine Learning Engineer

Arlington, VA ยท On-site

$77K - $176K/yr

R0242757 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Arlington, VA ยท On-site

$77K - $176K/yr

R0245170 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Arlington, VA ยท On-site

$77K - $176K/yr

Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct ...

Machine Learning Engineer

Arlington, VA ยท On-site

$77K - $176K/yr

R0242766 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Showing results 41-60

Mlops Machine Learning Engineer information

See Washington, DC salary details

$35.7K

$145.8K

$219.2K

How much do mlops machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for mlops machine learning engineer in Washington, DC is $145,843.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,000.00 and $175,600.00 per year, depending on experience, location, and employer.

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

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

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, 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, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.
Infographic showing various Mlops Machine Learning Engineer job openings in Washington, DC as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $145,843 per year, or $70.1 per hour.

AI/Machine Learning Engineer

Initiate Government Solutions

Washington, DC โ€ข Remote

$129K - $155K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 11 days ago


Job description

Description

Founded in 2007, Initiate Government Solutions (IGS) is a Woman-Owned Small Business and a fully remote IT services provider supporting federal partners nationwide. We deliver innovative Enterprise IT and Health Services solutions with a strong focus on data analytics, health informatics, cloud migration, AI, and the modernization of federal information systems.


Our vision is to be a health IT trendsetter, continuing to solve the nation's most challenging healthcare IT issues by conceiving, designing, and building solid, creative, and innovative open-source solutions.ย 


Our mission is to innovate, design, and deliver tailored solutions that balance technical advancement with cost-awareness while providing exceptional service.


IGS is currently pipelining for a remote AI/Machine Learning Engineer to support our work within the federal healthcare industry.ย  Candidates will be contacted as opportunities become available for further consideration.ย 


Assignment of Work and Travel:

This is a remote access assignment. The Candidate will work remotely daily and will remotely access VA systems and therein use approved VA provided communications systems. Travel is not required; however, the candidate may be required to attend onsite client meetings as requested.


The AI/Machine Learning Engineer will work alongside a team of highly skilled developers and engineers in the development of AI applications. A motivated and qualified candidate will not only have hands-on development experience in (JavaScript, Python or Java) but also a willingness to collaborate with teams to solve problems. Together we're accelerating our client's digital transformation through the building and deployment of data-driven, scalable AI solutions.


Responsibilities and Duties (Included but not limited to):

  • Design, develop, and deploy machine learning and deep learning models to support clinical decision-making, predictive analytics, and health outcomes research.
  • Fine-tune models for high performance using healthcare-specific data, including EHRs, claims, imaging, and structured/unstructured text.
  • Collaborate with data engineers to clean, preprocess, and normalize healthcare data in compliance with federal data standards (e.g., HL7, FHIR).
  • Build scalable ML pipelines that integrate with federal data platforms and cloud services (e.g., VA's Lighthouse API, Azure Government, AWS GovCloud).
  • Ensure AI/ML solutions meet federal regulations, including HIPAA, FISMA, FedRAMP, and VA Information Security requirements.
  • Implement differential privacy, encryption, and access controls to safeguard sensitive health data.
  • Contribute to the development of governance frameworks to ensure transparent, explainable, and bias-mitigated models.
  • Document model lifecycle, from training to deployment, including risk assessments, validation reports, and audit trails.
  • Work cross-functionally with program managers, clinicians, data scientists, and software developers to identify opportunities for AI/ML applications that improve healthcare delivery and veteran outcomes.
  • Present complex machine learning findings in a way that is actionable and aligned with federal healthcare program goals.
  • Stay updated on the latest developments in AI/ML applications for public health and healthcare operations.
  • Prototype and test emerging AI technologies (e.g., NLP for clinical text, computer vision for imaging diagnostics) for possible integration into government systems.
  • Monitor deployed models for drift, accuracy, and operational effectiveness over time.
  • Maintain model retraining schedules based on new data inputs or policy changes.
  • Prepare comprehensive documentation and reports for internal stakeholders and external oversight (e.g., OMB, GAO, IG audits).
  • Develop dashboards and visualizations to track performance metrics, patient outcomes, and utilization trends impacted by AI/ML tools.ย 

Requirements

  • Bachelor's degree or higher in one of the following disciplines, Computer Science, Data Science, Artificial Intelligence / Machine Learning, Mathematics / Statistics, Biomedical Engineering, Health Informatics, Electrical or Computer Engineering
  • 4+ years of experience in software and machine learning engineering.
  • Strong knowledge of natural language processing (NLP) and transformer models.
  • 5+ years proficiency in Python and hands-on experience with ML libraries like TensorFlow, PyTorch, or Hugging Face Transformers.
  • Proven experience building scalable, cloud-based AI/ML solutions and enhancing custom question answering mapping/workflows.
  • Expertise in the full ML pipeline, including data processing, model training, serving, and monitoring.
  • Knowledge of NLP architectural strategies such as Retrieval-Augmented Generation, Knowledge Graphs, and Agentic Graphs.
  • Expertise in MLOps best practices, including Infrastructure as Code (IaC), CI/CD pipelines tailored for ML workflows, model version control, and real-time performance monitoring to ensure scalable and reliable AI/ML systems.
  • Familiarity with federal AI governance frameworks and compliance standards (e.g., NIST AI RMF, FedRAMP) is a plus.
  • Passion for developing team-oriented solutions to complex engineering problemsย 
  • Excellent communication skills and attention to detail
  • Analytical mind and problem-solving aptitude
  • Ability to obtain and maintain a Public Trust
  • Strong organizational skills

Preferred Qualifications and Core Competencies:

  • Master's degree in one of the above-mentioned fields
  • Preferred Tools & Environments: Python, R, TensorFlow, PyTorch, Scikit-learn, AWS (SageMaker), Azure ML, Databricks, Apache Spark, Power BI, Tableau, Plotly, Git, GitHub/GitLab
  • Active VA Public Trust
  • Prior experience supporting a VA program
  • Prior, successful experience working in a remote environment

Successful IGS employees embody the following Core Values:

  • Integrity, Honesty, and Ethics: We conduct our business with the highest level of ethics. Doing things like being accountable for mistakes, accepting helpful criticism, and following through on commitments to ourselves, each other, and our customers.ย 
  • Empathy, Emotional Intelligence: How we interact with others including peers, colleagues, stakeholders, and customers' matters. We take collective responsibility to create an environment where colleagues and customers feel valued, included, and respected. We work within a diverse, integrated, and collaborative team to drive towards accomplishing the larger mission. We conscientiously and meticulously learn about our customers' and end-users' business drivers and challenges to ensure solutions meet not only technical needs but also support their mission.
  • Strong Work Ethic (Reliability, Dedication, Productivity): We are driven by a strong, self-motivated, and results-driven work ethic. We are reliable, accountable, proactive, and tenacious and will do what it takes to get the job done.ย 
  • Life-Long Learner (Curious, Perspective, Goal Oriented): We challenge ourselves to continually learn and improve ourselves. We strive to be an expert in our field, continuously honing our craft, and finding solutions where others see problems.

Compensation: There are a host of factors that can influence final salary, including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications.


Benefits: Initiate Government Solutions offers competitive compensation and a robust benefits package, including comprehensive medical, dental, and vision care, matching 401K and profit sharing, paid time off, training time for personal development, flexible spending accounts, employer-paid life insurance, employer-paid short and long term disability coverage, an education assistance program with potential merit increases for obtaining a work-related certification, employee recognition, and referral programs, spot bonuses, and other benefits that help provide financial protection for the employee and their family.


Initiate Government Solutions participates in the Electronic Employment Verification Program.ย