AI/ML Engineer, Lead

AI/ML Engineer, Lead The Opportunity:  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.

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. Join us. The world can’t wait.  You Have:   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 If You Have:   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 Vetting: Applicants selected will be subject to a