AI ML Engineer Jobs in Virginia
AI ML Engineer jobs in Virginia are concentrated in the Northern Virginia corridor, Richmond, and the Hampton Roads metro, where federal contractors, defense technology firms, and cloud infrastructure companies drive sustained demand for machine learning, natural language processing, and computer vision expertise. Employers like Booz Allen Hamilton, Leidos, and Amazon Web Services maintain deep engineering footprints across the state and regularly hire ai ml engineers at levels from early-career to principal. Northern Virginia in particular anchors one of the most active markets for this role in the country, fueled by federal agency work and hyperscale data center operations. Find a role that fits below and apply directly.
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Company Overview
Pantheon Data (a Kenific Holding company) is a private, small business based in the Washington, DC, area. Pantheon Data was founded in 2011, initially providing acquisition and supply chain management services to the US Coast Guard. Our service offerings have grown in the past ten years, including infrastructure resiliency, contact center operations, information technology, software engineering, program management, strategic communications, engineering, and cybersecurity. We have also grown our customer base to include commercial clients. The company has used this experience to expand our service offerings to other agencies within the Department of Homeland Security (DHS), the Department of Defense (DoD), and other Federal Civilian Agencies.
Position Overview
We are seeking a hands-on Data Engineer to help design, build, and operate the data foundations that support advanced analytics, AI/ML, and intelligent document processing solutions. The right candidate is a strong engineer who understands how data moves through real systems: ingestion, orchestration, transformation, quality checks, storage, query patterns, operational monitoring, and delivery to downstream applications. This person should be comfortable working across structured, semi-structured, and unstructured data held in relational, graph, and vector data stores, and should bring the judgment to build pipelines that are reliable, explainable, maintainable, and useful to the engineering teams and products that depend on them.
The ideal candidate has strong Python and SQL skills, understands when data should be modeled for operational use versus analytical use, and can reason clearly about batch processing, event-driven pipelines, data quality, lineage, and downstream consumption. They should be able to become productive quickly in a complex engineering environment, ask good questions, and build systems that other engineers can trust and extend. Experience with AWS, graph databases and query languages such as AQL, vector search, document data, or AI/ML data pipelines is valuable, but the core requirement is strong data engineering judgment: knowing how to move, structure, validate, and serve data reliably in support of real products and mission needs.
This role owns the data pipelines and data stores. You will partner closely with the Machine Learning Engineers, who own models, retrieval, extraction, and evaluation: they define how documents are chunked, embedded, enriched, and retrieved, and you build and run the pipelines and stores that apply those designs reliably at scale.
Responsibilities
- Design, build, and maintain reliable data pipelines for structured, semi-structured, and unstructured data sources.
- Develop Python-based processing workflows for data ingestion, normalization, validation, enrichment, and transformation.
- Work with SQL and relational data stores to support transactional, analytical, and application-facing use cases.
- Work with graph databases and query languages such as ArangoDB/AQL to model relationships between entities, documents, and metadata, and to support traversal and relationship-driven queries.
- Build and operate vector data pipelines and stores: run embedding generation at scale; manage vector index builds, refreshes, backfills, and re-embedding when models change; and serve the lookups that retrieval and RAG workloads depend on, implementing the chunking, embedding, and metadata specifications defined by the Machine Learning Engineers.
- Help design data models and storage patterns appropriate to the workload, including OLTP, OLAP, object storage, document-oriented, search, vector, or graph-oriented patterns when applicable.
- Implement orchestration and scheduling for repeatable data workflows using tools such as Airflow, AWS Step Functions, Dagster, Prefect, Glue workflows, or similar technologies.
- Build automated quality checks, reconciliation logic, validation reports, and operational alerts so data issues are detected early and can be diagnosed quickly.
- Support data pipelines that feed AI/ML, retrieval, document intelligence, analytics, and application workflows.
- Collaborate with machine learning engineers, software engineers, cloud engineers, and product stakeholders to turn ambiguous data problems into working software.
- Write maintainable code, participate in code reviews, document data flows, and contribute to engineering standards for testing, deployment, observability, and version control.
- Help improve the velocity of a growing engineering team by taking ownership of well-scoped data engineering work while continuing to grow into broader system ownership.
Required Skills and Experience
- Bachelor's degree in Computer Science, Engineering, or a related technical field from an accredited university.
- 5+ years of professional hands-on data engineering, software engineering, analytics engineering, or closely related experience.
- Strong Python programming skills, including experience writing maintainable production-oriented code rather than only notebooks or one-off scripts.
- Strong SQL skills and practical understanding of data modeling, query performance, joins, indexing, schemas, normalization/denormalization, and data quality.
- Understanding of core data engineering concepts, including batch processing, event-driven workflows, ETL/ELT, orchestration, idempotency, retries, backfills, lineage, and failure handling.
- Working knowledge of OLTP versus OLAP systems and the tradeoffs between transactional databases, analytical stores, object storage, search-oriented systems, and graph and vector databases.
- Experience building or supporting data pipelines that move data between systems, such as APIs, databases, files, object storage, queues, warehouses, or downstream applications.
- Ability to reason about data correctness, schema changes, validation, reconciliation, duplicate handling, missing data, and operational recovery.
- Comfortable working with Git, pull requests, code review, issue tracking, documentation, and collaborative software development practices.
- Strong communication skills and the ability to explain data flow, design choices, limitations, and tradeoffs to both technical and non-technical stakeholders.
- Ability to work effectively in a distributed, cross-functional engineering environment and produce high-quality work with limited hand-holding.
- Ability to work effectively remotely in cross-functional teams.
- Ability to meet deadlines and produce quality work.
- Proficient in Microsoft Suite software including Outlook, Word, Excel, SharePoint, and PowerPoint.
Preferred Skills and Experience
- AWS data services such as S3, Lambda, Glue, Athena, Step Functions, SQS/SNS, Kinesis, EMR, RDS, DynamoDB, Redshift, OpenSearch, or CloudWatch.
- Experience with workflow orchestration tools such as Airflow, Dagster, Prefect, AWS Step Functions, Glue, or similar.
- Experience with PySpark, Spark, Databricks, EMR, Snowflake, Redshift, or other distributed/analytical data platforms.
- Experience supporting AI/ML or RAG-style data workflows, including metadata enrichment, retrieval datasets, vector search, embeddings, evaluation datasets, or human validation workflows.
- Experience with document-oriented or unstructured data pipelines, including PDFs, OCR output, tables, forms, images, extracted text, metadata, or search indexes.
- Hands-on experience with graph databases and query languages such as ArangoDB/AQL, Neo4j/Cypher, or Gremlin, including graph-shaped data modeling.
- Experience with vector databases or vector search engines such as pgvector, OpenSearch, or similar, including index tuning and hybrid (vector + lexical) lookups.
- Experience with Docker, CI/CD, infrastructure as code, automated testing, logging, monitoring, and production support is a plus.
- Familiarity with data governance, access control, PII handling, auditability, lineage, and compliance-sensitive environments.
Clearance Requirements
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements. Candidates must have the ability to obtain and maintain a Secret clearance; an active Secret clearance is preferred.
Work Location: Reston, VA - Remote
- Our company prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely.
- If the position is remote or hybrid, you may periodically work from a Pantheon Data office location or client site.
- If this position is assigned to a Pantheon Data office location or client site, you'll work with colleagues and clients in person, as needed for specific client requirements.
Interview Requirement: Candidates who are local to the area should be prepared to participate in an in-person interview as part of the selection process. Candidates outside the local area may be considered for a virtual interview.
Compensation
The salary range for this position is $110,000 - $190,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
Benefits Overview
We are always looking for good people! Pantheon Data is committed to providing its employees with competitive salaries and benefits in order to increase employee satisfaction and productivity.In addition to our benefits, we also offer SmartBenefits through the Washington Metro Area Transportation Authority, where you specify an amount of your pre-tax wages be paid directly to your SmarTrip account. In some cases, tuition assistance may be available for continuing education expenses and certifications related to their position. Additional details may be found at https://pantheon-data.com/careers/
Pantheon Data Important Information
All qualified applicants will be considered for employment without regard to disability, status as a protected veteran, or any other status protected by applicable federal, state, local, or international law.
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to our Talent Team at Recruiting@pantheon-data.com or by phone (571) 363-4020.
This company uses E-Verify to confirm each employee's work authorization. For more information, click here E-Verify Participation Poster
Company Overview
Pantheon Data (a Kenific Holding company) is a private, small business based in the Washington, DC, area. Pantheon Data was founded in 2011, initially providing acquisition and supply chain management services to the US Coast Guard. Our service offerings have grown in the past ten years, including infrastructure resiliency, contact center operations, information technology, software engineering, program management, strategic communications, engineering, and cybersecurity. We have also grown our customer base to include commercial clients. The company has used this experience to expand our service offerings to other agencies within the Department of Homeland Security (DHS), the Department of Defense (DoD), and other Federal Civilian Agencies.
Position Overview
We are seeking a hands-on Data Engineer to help design, build, and operate the data foundations that support advanced analytics, AI/ML, and intelligent document processing solutions. The right candidate is a strong engineer who understands how data moves through real systems: ingestion, orchestration, transformation, quality checks, storage, query patterns, operational monitoring, and delivery to downstream applications. This person should be comfortable working across structured, semi-structured, and unstructured data held in relational, graph, and vector data stores, and should bring the judgment to build pipelines that are reliable, explainable, maintainable, and useful to the engineering teams and products that depend on them.
The ideal candidate has strong Python and SQL skills, understands when data should be modeled for operational use versus analytical use, and can reason clearly about batch processing, event-driven pipelines, data quality, lineage, and downstream consumption. They should be able to become productive quickly in a complex engineering environment, ask good questions, and build systems that other engineers can trust and extend. Experience with AWS, graph databases and query languages such as AQL, vector search, document data, or AI/ML data pipelines is valuable, but the core requirement is strong data engineering judgment: knowing how to move, structure, validate, and serve data reliably in support of real products and mission needs.
This role owns the data pipelines and data stores. You will partner closely with the Machine Learning Engineers, who own models, retrieval, extraction, and evaluation: they define how documents are chunked, embedded, enriched, and retrieved, and you build and run the pipelines and stores that apply those designs reliably at scale.
Responsibilities
- Design, build, and maintain reliable data pipelines for structured, semi-structured, and unstructured data sources.
- Develop Python-based processing workflows for data ingestion, normalization, validation, enrichment, and transformation.
- Work with SQL and relational data stores to support transactional, analytical, and application-facing use cases.
- Work with graph databases and query languages such as ArangoDB/AQL to model relationships between entities, documents, and metadata, and to support traversal and relationship-driven queries.
- Build and operate vector data pipelines and stores: run embedding generation at scale; manage vector index builds, refreshes, backfills, and re-embedding when models change; and serve the lookups that retrieval and RAG workloads depend on, implementing the chunking, embedding, and metadata specifications defined by the Machine Learning Engineers.
- Help design data models and storage patterns appropriate to the workload, including OLTP, OLAP, object storage, document-oriented, search, vector, or graph-oriented patterns when applicable.
- Implement orchestration and scheduling for repeatable data workflows using tools such as Airflow, AWS Step Functions, Dagster, Prefect, Glue workflows, or similar technologies.
- Build automated quality checks, reconciliation logic, validation reports, and operational alerts so data issues are detected early and can be diagnosed quickly.
- Support data pipelines that feed AI/ML, retrieval, document intelligence, analytics, and application workflows.
- Collaborate with machine learning engineers, software engineers, cloud engineers, and product stakeholders to turn ambiguous data problems into working software.
- Write maintainable code, participate in code reviews, document data flows, and contribute to engineering standards for testing, deployment, observability, and version control.
- Help improve the velocity of a growing engineering team by taking ownership of well-scoped data engineering work while continuing to grow into broader system ownership.
Required Skills and Experience
- Bachelor's degree in Computer Science, Engineering, or a related technical field from an accredited university.
- 5+ years of professional hands-on data engineering, software engineering, analytics engineering, or closely related experience.
- Strong Python programming skills, including experience writing maintainable production-oriented code rather than only notebooks or one-off scripts.
- Strong SQL skills and practical understanding of data modeling, query performance, joins, indexing, schemas, normalization/denormalization, and data quality.
- Understanding of core data engineering concepts, including batch processing, event-driven workflows, ETL/ELT, orchestration, idempotency, retries, backfills, lineage, and failure handling.
- Working knowledge of OLTP versus OLAP systems and the tradeoffs between transactional databases, analytical stores, object storage, search-oriented systems, and graph and vector databases.
- Experience building or supporting data pipelines that move data between systems, such as APIs, databases, files, object storage, queues, warehouses, or downstream applications.
- Ability to reason about data correctness, schema changes, validation, reconciliation, duplicate handling, missing data, and operational recovery.
- Comfortable working with Git, pull requests, code review, issue tracking, documentation, and collaborative software development practices.
- Strong communication skills and the ability to explain data flow, design choices, limitations, and tradeoffs to both technical and non-technical stakeholders.
- Ability to work effectively in a distributed, cross-functional engineering environment and produce high-quality work with limited hand-holding.
- Ability to work effectively remotely in cross-functional teams.
- Ability to meet deadlines and produce quality work.
- Proficient in Microsoft Suite software including Outlook, Word, Excel, SharePoint, and PowerPoint.
Preferred Skills and Experience
- AWS data services such as S3, Lambda, Glue, Athena, Step Functions, SQS/SNS, Kinesis, EMR, RDS, DynamoDB, Redshift, OpenSearch, or CloudWatch.
- Experience with workflow orchestration tools such as Airflow, Dagster, Prefect, AWS Step Functions, Glue, or similar.
- Experience with PySpark, Spark, Databricks, EMR, Snowflake, Redshift, or other distributed/analytical data platforms.
- Experience supporting AI/ML or RAG-style data workflows, including metadata enrichment, retrieval datasets, vector search, embeddings, evaluation datasets, or human validation workflows.
- Experience with document-oriented or unstructured data pipelines, including PDFs, OCR output, tables, forms, images, extracted text, metadata, or search indexes.
- Hands-on experience with graph databases and query languages such as ArangoDB/AQL, Neo4j/Cypher, or Gremlin, including graph-shaped data modeling.
- Experience with vector databases or vector search engines such as pgvector, OpenSearch, or similar, including index tuning and hybrid (vector + lexical) lookups.
- Experience with Docker, CI/CD, infrastructure as code, automated testing, logging, monitoring, and production support is a plus.
- Familiarity with data governance, access control, PII handling, auditability, lineage, and compliance-sensitive environments.
Clearance Requirements
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements. Candidates must have the ability to obtain and maintain a Secret clearance; an active Secret clearance is preferred.
Work Location: Reston, VA - Remote
- Our company prioritizes the benefits of flexibility and collaboration, whether that happens in person or remotely.
- If the position is remote or hybrid, you may periodically work from a Pantheon Data office location or client site.
- If this position is assigned to a Pantheon Data office location or client site, you'll work with colleagues and clients in person, as needed for specific client requirements.
Interview Requirement: Candidates who are local to the area should be prepared to participate in an in-person interview as part of the selection process. Candidates outside the local area may be considered for a virtual interview.
Compensation
The salary range for this position is $110,000 - $190,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
Benefits Overview
We are always looking for good people! Pantheon Data is committed to providing its employees with competitive salaries and benefits in order to increase employee satisfaction and productivity.In addition to our benefits, we also offer SmartBenefits through the Washington Metro Area Transportation Authority, where you specify an amount of your pre-tax wages be paid directly to your SmarTrip account. In some cases, tuition assistance may be available for continuing education expenses and certifications related to their position. Additional details may be found at https://pantheon-data.com/careers/
Pantheon Data Important Information
All qualified applicants will be considered for employment without regard to disability, status as a protected veteran, or any other status protected by applicable federal, state, local, or international law.
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to our Talent Team at Recruiting@pantheon-data.com or by phone (571) 363-4020.
This company uses E-Verify to confirm each employee's work authorization. For more information, click here E-Verify Participation Poster
See All 23 AI ML Engineer Jobs in Virginia
Find roles in Virginia that match your experience and apply in just a few clicks.
Find AI ML Engineer JobsAI ML Engineer Jobs by City in Virginia
Where Virginia roles are concentrated, by current openings.
AI ML Engineer Job Market in Virginia
A snapshot from current Virginia openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
What Virginia Employers Look For
The qualifications that appear most often in AI ML engineer jobs across Virginia.
- Bachelor's or master's degree in computer science, data science, or a related field
- Proficiency in Python and at least one ML framework such as TensorFlow or PyTorch
- Experience designing, training, and deploying machine learning models in production environments
- Familiarity with cloud platforms including AWS, Azure, or Google Cloud for ML workloads
- Active or eligible DoD security clearance preferred by many Virginia federal contractors
- Strong grasp of MLOps practices including model versioning, monitoring, and CI/CD pipelines
AI ML Engineer Jobs in Virginia: Frequently Asked Questions
How do you become a ai ml engineer in Virginia?
There is no state-issued license required to work as an ai ml engineer in Virginia. The standard path is a bachelor's degree in computer science, mathematics, or data science, followed by demonstrated project experience through a portfolio, graduate study, or internships. Virginia's federal contractor market places additional weight on security clearance eligibility, so candidates who pursue a clearance early gain a significant edge with Northern Virginia employers.
Which companies hire ai ml engineers in Virginia?
Employers hiring ai ml engineers in Virginia right now include Booz Allen Hamilton, SAIC, and Accenture Federal Services, based on current listings on Migrate Mate as of November 2026. Virginia's combination of federal agencies, defense contractors, and major cloud operations means hiring is spread across both government-facing firms and commercial technology teams.
Which Virginia cities have the most ai ml engineer jobs?
Chantilly, McLean, and Reston are the cities with the most ai ml engineer openings in Virginia. Northern Virginia leads because of its dense concentration of federal agencies, intelligence community contractors, and Amazon's cloud headquarters in the Arlington and Tysons areas, while Richmond and Hampton Roads reflect growing commercial and defense technology operations in those metros.
Are there remote ai ml engineer jobs in Virginia?
Yes, and more than most fields. About 79% of ai ml engineer openings tied to Virginia are remote or hybrid as of November 2026, reflecting how well the role's core work translates to distributed teams. Model development, data pipeline work, and research-oriented roles tend to be the most remote-friendly, while roles tied to classified federal programs typically require on-site presence at cleared facilities.
How can I get hired as a ai ml engineer in Virginia with little or no experience?
The most realistic entry path is through an internship or a junior data scientist role at one of Virginia's large federal contractors, where structured rotational and new-hire programs regularly bring in candidates with strong academic projects but limited professional experience. Firms like Booz Allen Hamilton and Leidos run dedicated early-career and associate programs that accept candidates from Virginia universities. Building a public portfolio of end-to-end ML projects and pursuing an AWS or Azure machine learning certification strengthens applications when professional experience is thin.
Where can I find and apply to ai ml engineer jobs in Virginia?
You can find and apply to ai ml engineer jobs in Virginia on Migrate Mate, which lists current openings across the state. Search the available roles, find the ones that fit your background and location, and apply directly to each position.
See All 23 AI ML Engineer Jobs in Virginia
Find roles in Virginia that match your experience and apply in just a few clicks.
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