Be Challenged and Make a Difference
In a world of technology, people make the difference. We believe if we invest in great people, then great things will happen. At AnaVation, we provide unmatched value to our customers and employees through innovative solutions and an engaging culture.
Benefits
Generous cost sharing for medical insurance for the employee and dependents
100% company paid dental insurance for employees and dependents
100% company paid long-term and short-term disability insurance
100% company paid vision insurance for employees and dependents
401k plan with generous match and 100% immediate vesting
Competitive Pay
Generous paid leave and holiday package
Tuition and training reimbursement
Life and AD&D Insurance
About AnaVation
AnaVation is the leader in solving the most complex technical challenges for collection and processing in the U.S. Federal Intelligence Community. We are a US owned company headquartered in Chantilly, Virginia. We deliver groundbreaking research with advanced software and systems engineering that provides an information advantage to contribute to the mission and operational success of our customers. We offer complex challenges, a top-notch work environment, and a world-class, collaborative team.
If you want to grow your career and make a difference while doing it, AnaVation is the perfect fit for you!
AnaVation is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
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- Active Top Secret (TS) clearance with eligibility for Sensitive Compartmented Information (SCI) and ability to obtain a Counterintelligence (CI) Polygraph.
- Bachelor's degree or master's degree with 10 years of experience specializing in AI/ML engineering
- Hands-on experience deploying production AI/GenAI and LLM systems, including embeddings, multi-model and multimodal orchestration, reasoning, memory, and tool execution.
- Proficiency with PyTorch, TensorFlow, scikit-learn, and cloud platforms (AWS, Azure, or GCP), with practical MLOps experience using MLflow, Azure ML, or Vertex AI.
- Experience implementing NLP, computer vision, reinforcement learning, temporal, spatial, geographic, or quantitative AI capabilities, including named entity recognition, contextual entity extraction, and entity resolution.
- Experience developing multi-step retrieval and structured output workflows with source citations, provenance, and traceable analytic results.
- Expertise in data architecture integrating vector, graph, and relational databases, including automated ETL pipeline development, scheduling, optimization, complex parsing, and schema-aligned ingestion and normalization of structured and unstructured data.
- Experience implementing data-layer Attribute-Based Access Control (ABAC) and end-to-end data lineage tracking.
- Experience supporting air-gapped, hybrid, and on-premises environments, including locally hosted GPU servers, hyperconverged infrastructure, container orchestration, distributed systems, and multitenant Cross-Domain Solutions (CDS).
- Proficiency in full-stack application development and integration using TypeScript, React, Python, FastAPI, LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes to connect user interfaces, backend services, middleware, retrieval systems, databases, and AI/GenAI services.
- Experience delivering and maintaining solutions in agile engineering environments.
- Requires an active Top Secret (TS) clearance and the ability to obtain SCI access with a CI polygraph.
- Requires the ability to work on-site in Chantilly, VA.
- Experience in federal, defense, or highly regulated environments.
- Knowledge of ethical AI, governance frameworks, and secure development practices.
- Skills in large-scale optimization, GPU acceleration, or distributed training.
- Experience building APIs, microservices, and automated ML workflows.
Please note: The listed salary range reflects our market-based compensation structure. Final compensation will depend on business needs, local market conditions, internal equity, and the selected candidate’s skills, education, and experience.