Senior Machine Learning Engineer, webAI

• Active US Security clearance
• 4+ years of experience in applied AI, ML engineering, or production AI systems
• Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers
• Proven experience deploying AI models across cloud, edge, and mobile hardware environments
• Expertise in model compression and optimization (quantization, pruning, distillation)
• Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone)
• Familiarity with multi-modal models and synthetic data generation methods
• Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments
• We seek individuals who exemplify the following:
• Truth - Emphasizing transparency and honesty in every interaction and decision
• Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients
• Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement
• Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others

Desirable

• We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments
• You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments
• Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems
• Productionize AI models from research prototypes into scalable, deployable systems used in real world applications
• Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization
• Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration
• Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval
• Work with multi-modal AI systems across computer vision, audio, and natural language domains
• Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions