Remote Forward-Deployed AI Engineer Lead

Guidehouse; McLean (VA); Remote; USD 149,000 - 248,000; Full time

Benefits offered by this job

Medical insurance

401(k) retirement plan

Parental leave

Job summary

Guidehouse is seeking a Forward Deployed AI Engineer Lead to work with clients and delivery teams, turning complex needs into production-grade AI solutions. You will operate at the intersection of AI, software, and data, delivering prototypes, integrated solutions, and scalable patterns.

You will mentor engineers, lead technical discovery, and drive adoption of agentic AI practices, MCP, and RAG in enterprise environments while balancing security, performance, and maintainability.

Qualifications

• Seven (7) or more years of overall work experience.
• Experience in software engineering, data engineering, AI/ML engineering, or related technical role.

Responsibilities

• Lead forward deployed engagements with clients to translate needs into working AI solutions.
• Prototype, test, and deploy generative AI and agentic AI applications in enterprise environments.
• Mentor and develop junior engineers into effective Forward Deployed Engineers.
• Design MCP-based integrations, A2A patterns, RAG, and tooling for scalable solutions.
• Communicate architecture decisions and trade-offs to both technical and non-technical stakeholders.

Skills

AI/ML engineering

Solution delivery

Client communication

Leadership

Mentoring & coaching

Forward Deployed engineering

Education

Bachelor’s degree

Master’s degree preferred

Tools

Claude APIs

LangGraph

LangChain

Semantic Kernel

Azure OpenAI / enterprise AI platforms

Job description

Guidehouse is seeking a Forward Deployed AI Engineer Lead to work with clients and delivery teams, turning complex needs into production-grade AI solutions. You will operate at the intersection of AI, software, and data, delivering prototypes, integrated solutions, and scalable patterns.

You will mentor engineers, lead technical discovery, and drive adoption of agentic AI practices, MCP, and RAG in enterprise environments while balancing security, performance, and maintainability.