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Machine Learning Ops Engineer Jobs in Virginia (NOW HIRING)

Senior ML Ops Engineer

Alexandria, VA · On-site

$107.65K - $171.95K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? About ...

Machine Learning Engineer Chantilly, VA We are seeking a Machine Learning Engineer to join our team ... We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...

GCP/AWS Machine Learning Engineer Freddie Mac iLab is currently looking for Machine Learning Engineers in its Innovation Labs - Tech Strategy team. In this position, you will be responsible for ...

Machine Learning Engineer D.C. Area About the Position As a member of our Engineering team, you will work with a tight, highly skilled machine learning / data science team dedicated to the ...

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Machine Learning Ops Engineer information

See Virginia salary details

$31.2K

$127.7K

$191.8K

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

As of May 28, 2026, the average yearly pay for machine learning ops engineer in Virginia is $127,665.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,700.00 per year, depending on experience, location, and employer.

What is a Machine Learning Ops Engineer job?

A Machine Learning Ops Engineer (MLOps Engineer) focuses on deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and software engineering, ensuring models run efficiently, reliably, and at scale. Their responsibilities include automating workflows, managing infrastructure, and ensuring CI/CD pipelines for ML models. They work with tools like Kubernetes, Docker, and cloud platforms to streamline model deployment. Ultimately, an MLOps Engineer ensures that machine learning models are operationalized and continuously improved in a real-world environment.

What are the key skills and qualifications needed to thrive in the Machine Learning Ops Engineer position, and why are they important?

To thrive as a Machine Learning Ops Engineer, you need a solid grasp of machine learning concepts, cloud platforms, software engineering, and DevOps practices, typically supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, TensorFlow, CI/CD pipelines, and certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving skills, communication, and the ability to work collaboratively across data science and engineering teams set top candidates apart. These skills ensure reliable deployment, scalability, and optimization of machine learning models in production environments.

What does a typical day look like for a Machine Learning Ops Engineer?

A typical day for a Machine Learning Ops Engineer involves collaborating with data scientists to streamline the deployment of models, building and maintaining scalable infrastructure on cloud services, and automating workflows with CI/CD tools. You may troubleshoot issues in production environments, monitor model performance, and implement solutions for model versioning and retraining. Often, you’ll work closely with software engineers, DevOps teams, and data analysts to ensure seamless integration of machine learning solutions into products. This cross-functional role keeps you engaged with cutting-edge technology and provides opportunities to influence both technical and business outcomes.
What are the most commonly searched types of Machine Learning Ops Engineer jobs in Virginia? The most popular types of Machine Learning Ops Engineer jobs in Virginia are:
Senior ML Ops Engineer

Senior ML Ops Engineer

RELX

Alexandria, VA • On-site

$107.65K - $171.95K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization?

Are you looking to drive cutting edge products that have a true societal impact?

About the team, this team that powers Elsevier’s Health platforms: Clinical Key AI, Sherpath AI, and AI-driven automated clinical and content workflows. You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world’s largest medical and scholarly landscapes.

About the role, as a Senior Machine Learning Engineer you’ll work on AI-based features (GenAI, Agentic AI, RAG, etc.) search/ranking quality, and knowledge graph aware retrieval while enforcing content rights and editorial confidentiality.

Key Responsibilities

ML & LLM Engineering, Search and Recommendation Engines

  • Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI).

  • Maintain and version model registries and artifact stores to ensure reproducibility and governance.

  • Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.

  • Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML.

  • Scale end-end custom Sagemaker pipelines.

  • Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.

  • Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs .

  • Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (faithfulness, grounding), and A/B testing.

  • Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.

  • Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems.

Collaboration

  • Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions

  • Collaborate and interface with Operations Engineers who deploy and run production infrastructure.

Qualifications

  • Current experience in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.

  • Strong Python, Java, and/or Scala experience will be considered a plus.

  • Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or Google)

  • Experience with Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr / Neo4j).

  • Experience in evaluating LLM models.

  • A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics.

  • Background in health technology and/or medical content workflows is preferred.

  • Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark.

  • Experience with large-scale data processing systems, e.g., Spark.

  • Experience with statistical analysis, machine learning theory and natural language processing.

Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world. Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.

U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates.

If performed in Maryland, the base pay range is $100,100 - $166,800.If performed in New Jersey, the base pay range is $107,646 - $171,954.

This job is eligible for an annual incentive bonus.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here (https://www.relx.com/careers/join-us/benefits) to access benefits specific to your location.

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RELX is a global provider of information-based analytics and decision tools for professional and business customers, enabling them to make better decisions, get better results and be more productive.

Our purpose is to benefit society by developing products that help researchers advance scientific knowledge; doctors and nurses improve the lives of patients; lawyers promote the rule of law and achieve justice and fair results for their clients; businesses and governments prevent fraud; consumers access financial services and get fair prices on insurance; and customers learn about markets and complete transactions.

Our purpose guides our actions beyond the products that we develop. It defines us as a company. Every day across RELX our employees are inspired to undertake initiatives that make unique contributions to society and the communities in which we operate.