AI/ML Engineer, Lead
$128,700–$292,000 year
On-siteAshburn, Virginia, United States
Job Summary
Design, develop, and deploy machine learning models and AI systems for large-scale production environments, leading end-to-end lifecycle processes from data exploration to model optimization. Architect scalable ML pipelines and infrastructure using modern frameworks and cloud technologies while collaborating with cross-functional stakeholders to translate business requirements into ML-driven solutions. Mentor junior engineers, establish best practices, and evaluate emerging AI technologies including LLMs, agentic AI, and retrieval-augmented generation. Optimize deployed models for accuracy and edge environments, ensuring robust pipeline integrations with data engineering teams. Work with law enforcement and homeland security clients to solve real-world challenges and define ML strategy.
Required Qualifications
- 8+ years of experience developing and deploying machine learning models in production environments
- Experience in Python and ML frameworks
- Experience with cloud platforms, such as AWS, Azure, or GCP, and tools for scalable ML systems, such as SageMaker, Azure ML, or Vertex AI
- Experience building data pipelines
- Experience with MLOps practices including CI/CD for ML, model versioning, monitoring, and deployment automation
- Knowledge of ML algorithms, statistics, model optimization, and evaluation methodologies
- Ability to design distributed systems and work with microservice-based architectures
- Ability to communicate complex technical concepts clearly to non-technical stakeholders
- Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
- Bachelor's degree in a Computer Science, Data Science, or Engineering field
Desired Qualifications
- Experience with LLMs, generative AI, RAG systems, and prompt engineering
- Experience building agentic AI systems, including multi-agent frameworks, autonomous agents, tool and function calling, orchestration libraries such as LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI
- Experience with edge AI optimization and deployment, including model quantization, pruning, and distillation, and deployment to edge and embedded hardware using frameworks such as TensorRT, ONNX Runtime, OpenVINO, or TensorFlow Lite
- Experience with open-source containerization and container orchestration technologies, including Docker and Kubernetes
- Experience with MLOps for production and machine learning workloads
- Possession of strong problem-solving skills
- Master's degree preferred; Doctorate degree a plus
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.