AI​/ML Engineer

Job Status: Contingent upon customer funding and clearance crossover

Location: Reston, VA or Bolling AFB, DC (onsite)

Clearance Required: 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).

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.

We are looking for an experienced and highly motivated AI/ML Engineer to join our team to continue the development and training of our in-house custom AI language model. Our model enables our customers to perform critical data labeling tasks over multi-media sources such as video, images, audio, and documents.

The AI/ML Engineer is the core mission specialist who develops, implements, and deploys innovative AI/ML and LLM-enabled capabilities to solve mission-critical problems. This role combines deep model engineering expertise with mission-focused innovation, creating new features and approaches that leverage AI/ML technologies to maximize mission impact.

• 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.

• 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…