TS/SCI Cleared AI/ML Engineer

About the role

Background Assured Consulting Solutions provides strategic and innovative solutions for customer needs across the business, technology, and organizational spectrum. As a member of our team, you will have the chance to work with customers that are both Government and industry leaders and technology innovators.

Responsibilities

• Develop and automate fine-tuning and model training pipelines using available tools or custom code.
• Develop innovative AI/ML and LLM-enabled solutions to address specific mission challenges and operational needs.
• Design, implement, and optimize machine learning models for new mission-critical use cases and features.
• Conduct research on novel modeling approaches, architectures, and techniques to maximize mission capability and competitive advantage.
• Work with mission leads and stakeholders to translate operational needs into technical AI/ML designs and implementation plans.
• Build and maintain MLOps and model deployment pipelines for experiment tracking, model versioning, and reliable production releases.
• Define and track model performance metrics aligned to mission success criteria and use evaluation findings to drive improvements.
• Integrate AI/ML model services into application workflows through APIs and production-ready interfaces.
• Partner with Data Integration Engineers to utilize curated training datasets, test corpora, and evaluation frameworks.
• Collaborate with Senior Software Engineers to operationalize AI/ML capabilities within secure, mission-focused application environments.
• Implement guardrails, monitoring, and fallback strategies for responsible and reliable AI/ML-enabled operations.
• Analyze model behavior, identify performance gaps, and innovate on approaches to improve quality, reliability, and mission impact.
• Document model designs, assumptions, training methodologies, evaluation results, and operational guidance for sustainability and knowledge transfer.
• Support production troubleshooting and performance optimization for mission-critical model-serving workloads.
• Contribute to technical standards and best practices for responsible, secure AI/ML engineering in mission environments.

Requirements

• Bachelor's degree or higher in a related STEM field, or equivalent experience.
• Hands-on experience in machine learning engineering, applied AI, or model development with demonstrated model deployment to production.
• Strong software engineering skills in Python for model development, training, inference, and experimentation workflows.
• Experience developing and evaluating machine learning models (supervised, unsupervised, or reinforcement learning) in production or mission-focused contexts.
• Demonstrated experience implementing and operationalizing LLM-enabled applications or features, including prompting strategies, retrieval approaches, and integration patterns.
• Experience building and maintaining MLOps infrastructure, including experiment tracking, model versioning, reproducibility, and continuous deployment practices.
• Experience defining model performance metrics, conducting model evaluation, and using evaluation results to drive improvements.
• Experience deploying and operating model services in containerized environments (for example OpenShift or Kubernetes).
• Demonstrated case studies or examples of innovative use of AI/ML to solve domain-specific or mission-critical problems.
• Demonstrated ability to communicate technical complexity, model assumptions, and performance limitations clearly to both technical and non-technical stakeholders.
• Understanding of secure development, secure AI practices, and deployment governance in controlled or classified environments.

Qualifications

• Must be a U.S. Citizen and possess a current and active TS/SCI clearance granted by the Department of Defense or an Intelligence Community agency.
• Must be able to pass a Counterintelligence (CI) Polygraph (current CI Polygraph a plus).

Skills

• Experience supporting DIA or comparable intelligence community mission environments and problem sets.
• Experience with AWS and C2E cloud environments for AI/ML workload and model serving.
• Experience with advanced model-serving frameworks, orchestration, or inference optimization.
• Familiarity with ontology-driven data modeling or semantic technologies (for example RDF, OWL, or knowledge graphs) for structured reasoning.
• Experience with retrieval-augmented generation (RAG), vector search, knowledge-grounded LLM approaches, or semantic search.
• Experience with multi-model or ensemble approaches for improved performance or robustness.
• Familiarity with DevSecOps practices and model release governance in secure environments.
• Experience evaluating and improving reliability, observability, and performance monitoring for mission-critical AI systems.

Benefits

Not specified.

Pay

Not specified.

Schedule

Not specified.