Hybrid AI/ML Engineer

at FTI - Frontier Technology Inc.

• Job title: AI/ML Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, PyTorch, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Raleigh, United States

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