AI/ML Engineer, Lead

Booz Allen Hamilton•Ashburn, VA

19h•$128,700 - $292,000

About The Position

As an AI/Machine Learning Engineer, you'll lead the design, development, and deployment of advanced AI and ML systems that support enterprise products and strategic initiatives. This role combines deep technical expertise with the ability to collaborate across engineering, product, and data teams. The ideal candidate has extensive experience building production-grade ML models, optimizing model performance, and guiding architectural decisions around scalable AI systems. As an experienced engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution.

Requirements

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

Nice To Haves

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

Responsibilities

Design, develop, and deploy machine learning models and AI systems for large-scale production environments.
Lead end-to-end ML lifecycle processes including data exploration, feature engineering, experimentation, model training, evaluation, and deployment.
Architect scalable ML pipelines and infrastructure using modern frameworks and cloud technologies.
Collaborate with cross-functional stakeholders to translate business requirements into ML-driven solutions.
Mentor junior engineers and contribute to best practices, coding standards, and technical excellence.
Evaluate and integrate emerging AI technologies, tools, and frameworks aligned with organizational goals.
Monitor and optimize deployed models for accuracy, performance, drift, reliability, and ethical considerations.
Partner with data engineering teams to ensure high-quality datasets and robust pipeline integrations.
Design, build, and deploy large language model (LLM) and agentic AI solutions, including multi-agent orchestration, tool use, autonomous workflows, and retrieval-augmented generation (RAG).
Optimize and deploy AI/ML models for edge environments, applying techniques such as quantization, pruning, and distillation to enable low-latency inference on resource-constrained and edge devices.
Work with us to solve real-world challenges and define ML strategy for law enforcement and homeland security clients.