AI Engineer (SME)

**Location:** Chantilly, VA **Security Clearance:** Active TS or higher [Required] w/ ability to obtain CI Poly **Job Type:** Full-Time **Target Salary Range\*:**$143,000 - $195,000 * 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 **Position Overview** ===================== 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. **Key Responsibilities** ======================== ### **AI/ML Development and Deployment** * 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. ### **Advanced AI and Multimodal Engineering** * 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. ### **Data Architecture and ETL** * 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. ### **Enterprise Infrastructure and Cross-Domain Solutions** * 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 multitenant cross-domain solutions (CDS). ### **Application Development and Integration** * 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. ### **Governance and Technical Leadership** * Ensure alignment with AI governance, documentation, ethical compliance, and security posture requirements. * Provide technical mentorship and drive engineering standardization across teams. **Qualifications** ================== ### **Education** * Bachelor’s degree required. ### **Experience** * Minimum of 10 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. ### **Skills** * 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. **Preferred Qualifications** ============================ * 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.