Forward Deployed AI Engineer Lead

Guidehouse•Tysons, VA

1d•$149,000 - $248,000

About The Position

As a Forward Deployed AI Engineer Lead, you will work directly with clients and multidisciplinary delivery teams to turn complex business and mission needs into practical, production-ready AI solutions. The role operates at the intersection of AI, software engineering, data, and client delivery, moving from ambiguous problems to working prototypes, integrated solutions, and scalable implementation patterns. The position combines hands-on engineering, client-facing technical leadership, innovation, and team development. You will lead complex forward deployed engagements and help develop Forward Deployed Engineers through hands-on mentorship, technical coaching, and repeatable engineering and delivery practices. You will lead the design, prototyping, testing, deployment, integration, and operational support of generative AI and agentic AI applications in enterprise environments. This includes agent architectures and integrations using MCP, A2A, RAG, tools, structured outputs, orchestration, context management, and human review where appropriate. You will advise clients, lead discovery and architecture sessions, work directly with users to refine solutions and encourage adoption, and balance usability, performance, security, maintainability, and responsible AI. You will also mentor engineers, provide technical direction and code review, and build reusable playbooks, standards, solution patterns, and accelerators. The role fosters experimentation, continuous learning, collaboration, technical excellence, and responsible AI development.

Requirements

Must be able to obtain and maintain a Federal Public Trust; candidates with an active Public Trust or suitability are preferred.
Seven or more years of overall work experience.
Experience in software engineering, data engineering, AI/ML engineering, solution engineering, or a related technical role.
Experience as a Forward Deployed Engineer, Forward Deployed Software Engineer, Forward Deployed AI Engineer, or in a comparable client-embedded engineering role, including translating ambiguous client requirements into working technical solutions.
Hands-on experience designing and building generative AI, large language model, or agentic AI solutions.
Hands-on experience with Claude APIs, Claude for Enterprise, Claude Code, tool use, Custom Skills, or comparable enterprise large language model platforms and developer tooling.
Hands-on experience with Model Context Protocol (MCP) for connecting AI applications or agents with tools, services, data sources, or enterprise systems.
Hands-on experience with agent-to-agent (A2A) and multi-agent solution patterns, including communication or coordination among specialized agents.
Hands-on experience designing and implementing RAG solutions, including enterprise knowledge retrieval, embeddings, vector search, and grounding LLM applications with authoritative data or content.
Hands-on experience designing agentic AI solutions using tool use, structured outputs, context management, orchestration, and human-in-the-loop patterns.
Strong software engineering experience using modern programming languages, APIs, services, and application development frameworks.
Experience integrating AI applications and agents with enterprise systems, databases, APIs, tools, and data sources.
Experience developing reliable, maintainable, testable, and well-documented production code.
Demonstrated experience mentoring and developing engineers, including coaching on engineering standards, technical problem-solving, solution delivery, and client-facing responsibilities.
Strong client communication and consulting skills, with demonstrated ability to work effectively with technical teams, business stakeholders, and senior client leaders.
Demonstrated ability to lead technical discovery and translate ambiguous business or mission problems into actionable technical requirements and working solutions.
Demonstrated experience leading complex technical implementations and providing technical direction to engineering or multidisciplinary teams.
Demonstrated ability to think innovatively, challenge conventional approaches, rapidly evaluate emerging technologies, and develop creative technical solutions to complex or undefined problems.
Strong written, verbal, presentation, facilitation, and interpersonal communication skills, including communicating technical concepts, architecture decisions, risks, recommendations, and trade-offs to technical and non-technical audiences.

Nice To Haves

Bachelor’s degree from an accredited university.
Master’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
Experience delivering AI, data, or software solutions within Federal Government, healthcare, financial services, or other highly regulated environments.
Experience deploying generative AI and agentic solutions on major cloud and enterprise AI platforms such as Microsoft Azure / Azure OpenAI, AWS / Amazon Bedrock, Google Cloud / Vertex AI, Databricks, or comparable platforms.
Experience with modern AI and agent development frameworks and patterns, such as LangGraph, LangChain, Semantic Kernel, or comparable orchestration frameworks.
Experience operationalizing generative AI solutions, including LLM evaluation, tracing, observability, prompt and model management, guardrails, cost optimization, and production monitoring.
Experience designing secure AI architectures incorporating identity and access management, secrets management, API security, data protection, authorization, auditability, and responsible AI controls.
Experience with containers, CI/CD pipelines, infrastructure-as-code, and cloud-native deployment patterns, including Docker, Kubernetes, GitHub Actions, Azure DevOps, or Terraform.
Experience with knowledge graphs, graph databases, advanced retrieval techniques, hybrid search, reranking, or other approaches that improve enterprise RAG quality and reliability.
Experience developing reusable AI accelerators, reference architectures, engineering frameworks, or platform capabilities for multiple teams or client engagements.
Experience contributing to technical strategy, solution architecture, estimation, proposal development, technical demonstrations, or client pursuit activities.
Relevant cloud, AI, data, or security certifications.
Active Federal Public Trust, Secret, or higher-level security clearance.

Responsibilities

Lead forward deployed engineering engagements, partnering with clients to understand workflows, identify high-value opportunities, and translate business and mission needs into working AI solutions.
Rapidly design, prototype, test, and deploy generative AI and agentic AI applications in complex enterprise environments.
Build AI solutions using large language models, APIs, developer tooling, and enterprise AI platforms.
Translate ambiguous and evolving requirements into practical technical designs, prototypes, and production-ready solutions.
Design and implement agentic architectures incorporating MCP, A2A communication, RAG, tool use, structured outputs, orchestration, context management, and human review where appropriate.
Lead technical discovery sessions, architecture discussions, demonstrations, and hands-on solution working sessions with client stakeholders.
Partner with users throughout implementation to gather feedback, diagnose challenges, refine solutions, and drive adoption.
Identify practical applications of emerging AI capabilities while balancing usability, performance, security, maintainability, and responsible AI.
Architect, build, and maintain integrations with internal and external systems using APIs, services, data pipelines, and modern application development practices.
Design AI applications and agents that interact with enterprise data, tools, business systems, APIs, and other agents.
Implement MCP-based integrations connecting AI applications and agents with enterprise tools, services, and data sources.
Implement A2A and multi-agent patterns enabling specialized agents to coordinate and communicate in broader workflows.
Implement RAG solutions, including enterprise knowledge retrieval, embeddings, vector search, grounding, and related retrieval patterns.
Apply modern software engineering practices to take solutions from prototype through testing, deployment, and operational support.
Work with relational databases, vector stores, retrieval systems, APIs, and related data patterns to support application state, knowledge retrieval, traceability, and auditability.
Implement testing, evaluation, logging, monitoring, and observability for AI-enabled applications and agentic workflows.
Diagnose technical and integration issues across applications, models, agents, APIs, data, and infrastructure.
Establish reusable technical patterns and accelerators to improve the quality and speed of future implementations.
Serve as a trusted senior technical advisor to clients on complex generative AI and agentic AI initiatives.
Build relationships with technical, business, data, and leadership stakeholders, connecting client needs with practical technical solutions.
Communicate technical concepts, solution options, risks, and trade-offs to technical teams, business stakeholders, and senior client leaders.
Facilitate discovery and working sessions that turn ambiguous client problems into actionable engineering decisions and solution approaches.
Present prototypes, demonstrations, architecture decisions, and technical recommendations to client stakeholders.
Ask effective questions, listen to client needs, and adapt technical approaches to business, mission, user, and technology considerations.
Constructively challenge assumptions and propose innovative approaches when conventional solutions do not adequately address the problem.
Balance rapid experimentation and creative problem-solving with the engineering discipline needed to deliver reliable, secure, maintainable, and scalable solutions.
Mentor and develop junior and mid-level engineers in technical engineering and client-facing delivery capabilities.
Coach team members on discovery, problem decomposition, requirements translation, prototyping, solution design, client communication, demonstrations, implementation, troubleshooting, and knowledge transfer.
Provide hands-on technical direction, architecture guidance, code review, pairing, and engineering mentorship.
Develop and promote reusable FDE playbooks, engineering standards, solution patterns, accelerators, and implementation practices.
Coach team members on agentic AI patterns, including MCP, A2A, RAG, tool use, orchestration, evaluation, and responsible AI.
Help engineers work effectively in ambiguous client environments and move independently from problem identification through working solution.
Create opportunities for junior team members to progressively take responsibility for discovery, client discussions, demonstrations, solution ownership, and technical leadership.
Foster experimentation, continuous learning, collaboration, technical excellence, and responsible AI development.
Capture and share lessons learned across engagements to improve FDE practices and delivery quality.