AI/ML Engineer (TS Clearance)

US·Restonmid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Develop and automate fine-tuning and model training pipelines using available tools or custom code.

Requirements Summary

Bachelor's degree or higher in a related STEM field, or equivalent experience Hands-on experience in machine learning engineering, applied AI,

Technical Tools

Machine Learning EngineerData

Nice to Have

~1 min read

Responsibilities

~2 min read

→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

~2 min read

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.

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.