AI Engineer (Senior)

Location: Chantilly, VA
Security Clearance: Active TS or higher [Required] w/ ability to obtain CI Poly
Job Type: Full-Time
Target Salary Range*: $160,000 - $180,000

*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

The AI Engineer (SME) builds and deploys scalable, production-ready AI, machine learning, and generative AI models, pipelines, and microservices. This role supports an in-house, custom-developed application, integrating advanced AI capabilities, enterprise data architectures, and distributed infrastructure. The SME also provides technical mentorship, promotes engineering standardization, and ensures alignment with AI governance and security requirements.

• Build scalable, production-ready AI/ML/GenAI models, pipelines, and microservices.
• Develop training, evaluation, and inference pipelines in Python using PyTorch, TensorFlow, or scikit-learn.
• Deploy and manage models using MLOps, CI/CD, containerization, and monitoring frameworks.
• Implement vector databases, embeddings, LLM applications, agentic AI, and tool-calling workflows.
• Optimize models for accuracy, latency, cost, and scalability across distributed environments.

• Orchestrate multiple models and modalities to support reasoning, perception, memory, and tool execution.
• Implement temporal, spatial, geographic, quantitative, and computer vision AI capabilities.
• Develop named entity recognition (NER), contextual entity extraction, and entity resolution capabilities.
• Implement multistep retrieval and structured output generation.
• Support complex data parsing and streamlined ingestion.
• Provide detailed source citations and traceable analytic outputs.

• Orchestrate vector, graph, and multiple database systems.
• Create, schedule, and optimize automated extract, transform, and load (ETL) pipelines.
• Implement attribute-based access control (ABAC) at the data layer.
• Track data provenance and end-to-end lineage.
• Support schema-aligned ingestion and normalization of diverse structured and unstructured data.

• Support air-gapped, hybrid, and on-premises operational environments.
• Manage container orchestration and distributed systems using Kubernetes.
• Support locally hosted GPU servers and hyperconverged nodes.
• Support multi-tenant cross-domain solutions (CDS).

• Develop user interfaces, backend services, and middleware for an in-house, custom-developed application.
• Use TypeScript, React, Python, FastAPI, LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
• Integrate reasoning components, retrieval systems, databases, and AI/GenAI services.
• Apply modern programming languages and frameworks to application development and integration.

• Ensure alignment with AI governance, documentation, ethical compliance, and security posture requirements.
• Provide technical mentorship and drive engineering standardization across teams.

• Bachelor’s degree required.

• Minimum of 8 years of specialized AI/ML engineering experience.
• Hands-on experience deploying production GenAI/LLM systems, embeddings, and vector databases.
• Practical experience with MLOps tools such as MLflow, Azure ML, or Vertex AI.
• Background in natural language processing (NLP), computer vision, or reinforcement learning.
• Experience working in agile engineering environments.

• Advanced Python skills and a strong understanding of algorithms, data structures, and distributed systems.
• Proficiency with PyTorch, TensorFlow, and scikit-learn.
• Proficiency with cloud platforms such as AWS, Azure, or GCP.
• Advanced AI, GenAI, multimodel orchestration, and multimodal engineering capabilities.
• Skills in data architecture, automated ETL, database orchestration, ABAC, and data lineage.
• Technical capabilities supporting air-gapped, hybrid, and on-premises environments, Kubernetes, GPU infrastructure, and multitenant CDS.
• UI, backend, and middleware engineering skills using the specified application technologies.

• 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.