Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
About the role
- AI/ML Engineer designing, building, and deploying advanced machine learning solutions for defense and national security. Collaborating with teams to integrate technology and ensure mission success.
Responsibilities
- Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives
- Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain
- Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration)
- Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid)
- Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services
- Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT)
- Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy
- Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots
- Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs
- Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.
Requirements
- Must be a U.S. citizen and be willing to obtain and maintain a secruity clearance
- 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments
- Professional experience within the Department of Defense (DoD/DoW) AI assurance, security, and deployment environments
- Strong Python development skills with hands-on experience building AI/ML solutions
- Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain
- Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent
- Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph)
- Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT
- Professional experience integrating AI capabilities into production systems or mission applications.
Benefits
- Competitive salary
- Health insurance
- Paid time off
- Flexible work arrangements
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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