Forward Deployed AI Engineer Lead

The Forward Deployed AI Engineer Lead role partners directly with clients and multidisciplinary delivery teams to convert complex business and mission requirements into production-ready generative and agentic AI solutions. This position provides hands-on technical leadership, client-facing discovery support, and mentorship to strengthen forward deployed engineering capability.

Key Responsibilities

• Lead forward deployed engineering engagements by partnering with clients to understand complex workflows, identify high-value opportunities, and translate business and mission needs into working AI solutions.
• Design, prototype, test, and deploy generative AI and agentic AI applications within complex enterprise environments.
• Build advanced AI solutions using large language models, APIs, developer tooling, and enterprise AI platforms.
• Convert ambiguous and evolving requirements into practical technical designs, prototypes, and production-ready solutions.
• Design and implement agentic architectures using Model Context Protocol (MCP), agent-to-agent (A2A) communication, retrieval-augmented generation (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 during implementation to gather feedback, diagnose challenges, refine solutions, and support adoption.
• Identify innovative, practical ways to apply 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.
• Design and implement MCP-based integrations connecting AI applications and agents with enterprise tools, services, and data sources.
• Design and implement A2A and multi-agent patterns that enable specialized agents to coordinate and communicate within broader workflows.
• Design and implement RAG solutions, including enterprise knowledge retrieval, embeddings, vector search, grounding, and related retrieval patterns.
• Use modern software engineering practices to move solutions from rapid prototype to 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 complex technical and integration issues across applications, models, agents, APIs, data, and infrastructure.
• Establish reusable technical patterns and accelerators to improve quality and delivery speed for future implementations.
• Serve as a trusted senior technical advisor to clients on complex generative AI and agentic AI initiatives.
• Build relationships across technical, business, data, and leadership stakeholders to connect client needs with practical technical solutions.
• Communicate complex technical concepts, solution options, risks, and trade-offs clearly to technical teams, business stakeholders, and senior client leaders.
• Facilitate discovery and working sessions that turn ambiguous client problems into actionable engineering decisions.
• Present prototypes, solution demonstrations, architecture decisions, and technical recommendations to client stakeholders.
• Ask effective questions, listen to client needs, and adapt technical approaches based on business, mission, user, and technology considerations.
• Provide mentorship and develop junior and mid-level engineers into effective Forward Deployed Engineers, including client-facing delivery capability.
• Coach team members on FDE best practices, including technical discovery, problem decomposition, requirements translation, rapid prototyping, solution design, client communication, demonstrations, 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 best practices.
• Coach team members on modern agentic AI engineering patterns, including MCP, A2A, RAG, tool use, orchestration, evaluation, and responsible AI practices.
• Capture and share lessons learned across engagements to continuously improve FDE practices and delivery quality.

Required Qualifications

• Must be able to OBTAIN and MAINTAIN a Federal PUBLIC TRUST; candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred.
• SEVEN (7+) or more years of overall work experience.
• Experience in software engineering, data engineering, AI/ML engineering, solution engineering, or a related technical role.
• Experience working 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 developing solutions using 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) and its use in 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 retrieval-augmented generation (RAG) solutions, including enterprise knowledge retrieval, embeddings, vector search, and grounding of 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 team members on engineering standards, technical problem-solving, solution delivery, and client-facing responsibilities.
• Strong client communication and consulting skills, including working 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 for technical and non-technical audiences.

Technologies

• Claude APIs, Claude for Enterprise, Claude Code
• Model Context Protocol (MCP)
• agent-to-agent (A2A)
• retrieval-augmented generation (RAG), embeddings, vector search, LLMs, large language models
• APIs, enterprise AI platforms
• tool use, structured outputs, orchestration, context management, human review
• relational databases, vector stores, logging, monitoring, observability, testing, evaluation
• Google Cloud, Vertex AI
• Microsoft Azure, Azure OpenAI
• AWS, Amazon Bedrock
• Databricks
• LangGraph, LangChain, Semantic Kernel
• Docker, Kubernetes
• GitHub Actions, Azure DevOps, Terraform

Benefits

• Medical, Rx, Dental & Vision Insurance
• Personal and Family Sick Time & Company Paid Holidays
• Position may be eligible for a discretionary variable incentive bonus
• Parental Leave and Adoption Assistance
• 401(k) Retirement Plan
• Basic Life & Supplemental Life
• Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts
• Short-Term & Long-Term Disability
• Student Loan PayDown
• Tuition Reimbursement, Personal Development & Learning Opportunities
• Skills Development & Certifications
• Employee Referral Program
• Corporate Sponsored Events & Community Outreach
• Emergency Back-Up Childcare Program
• Mobility Stipend

Additional Information

• Location: McLean, VA (onsite)
• Salary Range: USD 149,000 - 248,000 per yearly
• Job Family: Data Science Consulting
• Travel Required: Up to 10%
• Clearance Required: Ability to Obtain Public Trust

What Would Be Nice to Have

• 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 the quality and reliability of enterprise RAG solutions.
• Experience developing reusable AI accelerators, reference architectures, engineering frameworks, or platform capabilities that can be applied across 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.

About the Compensation

The annual salary range for this position is $149,000.00-$248,000.00.

What We Offer

Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects its commitment to creating a diverse and supportive workplace.