AI Engineering Manager – AI & Data Development (Health)

Job Family:

Data Science Consulting

Travel Required:

Up to 25%

Clearance Required:

None

What You Will Do:

As an AI Engineering Manager at Guidehouse, you will lead the design, development, deployment, and optimization of AI solutions, including foundational model fine-tuning and agentic workflow orchestration primarily within the healthcare sector and expanding into additional business verticals. You will play a critical role in shaping Guidehouse’s AI engineering strategy, ensuring solutions are production-grade, secure, compliant, and capable of delivering meaningful client and operational impact. Your expertise will help revolutionize healthcare delivery, enhance user experience, reduce operational costs, and maintain adherence to industry regulations such as HIPAA.

This role is a hands-on delivery leader and trusted client partner, responsible for independently owning projects/workstreams, driving agile execution, and translating healthcare business needs into scalable AI technology and data solutions. The role emphasizes delivery excellence, client impact, and collaboration.

Key Responsibilities

• AI & Agentic Systems Engineering

• Design, architect, and deploy enterprise-ready AI agents, including retrieval components, orchestration layers, policy-based routing, evaluation harnesses, and lifecycle observability.

• Develop and manage multi-provider abstraction layers across AI platforms (OpenAI, Anthropic, Google/Vertex AI, etc.) to support scalable and portable AI solutions.

• Build cloud-native AI systems, using Kubernetes, Docker, microservices, serverless computing, event-driven architectures, CI/CD, and comprehensive observability stacks.

• Platform & Workflow Integration

• Tailor and implement industry-specific agentic workflows (healthcare, finance, energy) to automate complex processes and drive measurable outcomes.

• Partner with cross-functional stakeholders to define use cases, rapidly prototype solutions, and deploy robust agentic workflows into production and client environments.

• Conduct design workshops, POCs, and joint engineering sessions to support client enablement and adoption of AI-driven solutions.

• Performance, Compliance & Governance

• Define and monitor key metrics for agent accuracy, safety, latency, cost-efficiency, and reliability.

• Ensure AI solutions comply with applicable industry and regulatory standards (HIPAA, SOC 2, etc.). Create and maintain detailed documentation across model development, t

• raining, fine-tuning, deployment, and operational processes.

• Leadership & Knowledge Sharing

• Provide technical leadership, mentorship, and guidance to junior engineers and contributing team members.

• Develop reusable patterns, best practices, and internal frameworks that influence Guidehouse’s AI engineering strategy and client roadmaps.

What You Will Need:

• US Citizenship or a Green Card is required

• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related field.

• A minimum of FIVE (5) years of engineering experience with cloud-native systems (APIs, microservices, containerization, serverless architectures).

• A minimum of ONE (1) year of deep, hands-on experience designing and deploying agentic AI systems (RAG, orchestration, tool use, workflow automation) in production.

• A minimum of THREE (3) years working with leading AI platforms (OpenAI, Claude, Vertex AI) and open-source LLM frameworks, including building abstraction layers for multi-provider pipelines.

• A minimum of FIVE (5) years of programming experience in Python, Java, or equivalent languages, with familiarity in logging, monitoring, and agent/LLM observability.

• A minimum of FIVE (5) years deploying production systems using CI/CD, infrastructure-as-code (Terraform, Helm), and modern monitoring/debugging tools.

• Delivered 3 to 5 medium or large-scale AI/LLM projects of significant business value.

• ML/LLM Engineering Skills

• Strong command of Python and modern AI/ML frameworks (TensorFlow, PyTorch, Hugging Face Transformers).

• Experience deploying and optimizing models using serving/inference frameworks (Triton, TensorRT, VLLM, TGI, etc.).

• Familiarity with MLOps practices for CI/CD, continuous training, observability, and automated monitoring.

• Domain & Compliance Knowledge

• Understanding of healthcare interoperability standards (FHIR, HL7, EDI) and reg