Machine Learning Engineer

• Partner with stakeholders to identify and refine AI/ML use cases and translate business needs into technical solutions
• Design, build, fine-tune, and evaluate ML and GenAI models, including LLMs, RAG, embeddings, and deep learning, using Vertex AI, Gemini, and open-source tools
• Develop end-to-end ML pipelines covering data ingestion, feature engineering, orchestration, and CI/CD for models and prompts
• Deploy scalable models and agents; manage monitoring, drift detection, and production troubleshooting
• Collaborate with data engineering teams on high-quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store
• Implement Responsible AI, security, governance, and compliance best practices including IAM, encryption, and auditing
• Work cross-functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions
• Perform experimentation, prototyping, exploratory data analysis, hyperparameter tuning, and documentation of pipelines and workflows

AI/ML Engineer

• Train, test, deploy, and maintain machine learning models
• Own and define the direction of critical AI solutions
• Implement AI that enables faster mission success
• Collaborate with machine learning engineers, data engineers, data scientists, solutions architects, and product owners
• Deliver AI solutions for a wide variety of use cases
• Guide clients on ML algorithms, tools, and frameworks
• Solve real-world challenges and define AI strategy for national security and the DoD market

• Build agentic systems
• Fine tune models
• Build reinforcement learning environments
• Apply best-fit AI models and technologies to mission-critical solutions
• Contribute to the direction of mission-critical solutions
• Collaborate with product teams, data engineers, and government stakeholders
• Help clients navigate AI algorithms, tools, and frameworks
• Solve real-world challenges and define AI strategy for the intelligence community
• Deliver customer-focused solutions for the federal government

AI and ML Engineer

• Design, build, and deliver machine learning models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Build and scale multi-tenant platforms for large-scale ML model training and serving
• Inform ML infrastructure decisions using modeling techniques and issues such as model choice, feature selection, hyperparameter tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate within cross-functional Agile teams to create and enhance big data and ML applications
• Retrain, maintain, and monitor production models
• Build or leverage cloud architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply CI/CD practices, test automation, and monitoring for ML model and application deployment
• Manage code to reduce vulnerabilities and govern models from a risk perspective
• Apply Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java

Senior Machine Learning Engineer – IC

• Architect large-scale data and document-processing platforms, from ingestion through downstream consumption
• Design robust patterns for document parsing, extraction, normalization, validation, storage, search, human review, and reprocessing
• Establish standards for schemas, metadata, lineage, provenance, data quality, reproducibility, and auditability
• Design reliable ingestion and transformation pipelines using APIs, files, batch processing, and streaming where appropriate
• Create platform patterns that enable Data Science and AI prototypes to become secure, maintainable production solutions
• Architect data and retrieval foundations supporting RAG, analytics, AI workflows, evaluation, and feedback loops
• Make architecture decisions involving scalability, reliability, security, privacy, cost, and operational complexity
• Lead technical design reviews, investigate production failures, mentor engineers, and work across Data Engineering, Data Science, DevOps/MLOps, Product, Security, and client teams
• Review and develop Python, SQL, schemas, APIs, and production data pipelines

Principal Data & ML Ops Architect

• Embed artificial intelligence into enterprise workflows and analytic processes to drive measurable gains in productivity, insight generation, and decision support within the Data Analytics & Research division.
• Lead applied, production-ready AI/ML initiatives that embed artificial intelligence into enterprise workflows.
• Partner with Data Engineering, Data Governance, and cross-functional business stakeholders.
• Ensure AI solutions are scalable, secure, compliant, and aligned with enterprise data strategy.
• Set standards for AI development practices.
• Mentor teammates on applied AI techniques.
• Translate complex machine learning capabilities into enterprise-grade systems that deliver lasting organizational value.
• Provide technical guidance, mentorship, and thought leadership within the organization and across the broader AI and higher education data community.
• Report to the Senior Director, Analytics Engineering.
• Operate as a senior individual contributor and technical authority.

Machine Learning Engineer jobs in Virginia

AI/ML Engineer