G
Forward Deployed AI Engineer Lead
Guidehouse Inc.
3 hours ago
Full-time
On-site
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 generative AI and agentic AI solutions. You will provide hands‑on engineering, client‑facing technical leadership, and mentorship to help build forward deployed engineering capability.
Role Overview
Lead forward deployed engineering engagements with direct client partnership.
Design, prototype, test, and deploy generative AI and agentic AI applications in complex enterprise environments.
Mentor engineers and strengthen client‑facing delivery capability across forward deployed teams.
Key Responsibilities
Lead forward deployed engineering engagements by understanding complex workflows, identifying high‑value opportunities, and translating business and mission requirements into working AI solutions.
Rapidly design, prototype, test, and deploy generative AI and agentic AI applications in complex enterprise settings.
Build advanced AI solutions using large language models, APIs, developer tooling, and enterprise AI platforms.
Convert ambiguous and evolving requirements into 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.
Facilitate technical discovery, architecture discussions, demonstrations, and hands‑on solution working sessions with client stakeholders.
Partner with end users during implementation to gather feedback, diagnose challenges, refine solutions, and support adoption.
Apply emerging AI capabilities to complex client challenges while balancing usability, performance, security, maintainability, and responsible AI.
Architect, build, and maintain integrations across 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 to enterprise tools, services, and data sources.
Design and implement A2A and multi‑agent patterns that enable specialized agents to coordinate as part of 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 from rapid prototyping 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 issues across applications, models, agents, APIs, data, and infrastructure.
Establish reusable technical patterns and accelerators to improve implementation quality and speed.
Act as a trusted senior technical advisor for complex generative AI and agentic AI initiatives.
Build relationships with technical, business, data, and leadership stakeholders and communicate complex concepts, options, risks, and trade‑offs clearly.
Mentor and develop junior and mid‑level engineers into effective Forward Deployed Engineers, including coaching on discovery, decomposition, requirements translation, prototyping, solution design, client communication, demonstrations, troubleshooting, and knowledge transfer.
Provide hands‑on technical direction, architecture guidance, code review, pairing, and engineering mentorship.
Develop reusable forward deployed engineering playbooks, standards, solution patterns, accelerators, and implementation best practices.
Coach team members on 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 forward deployed practices and delivery quality.
Required Qualifications
Must be able to obtain and maintain a Federal PUBLIC TRUST. Active Public Trust or suitability is 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 as a Forward Deployed Engineer, Forward Deployed Software Engineer, Forward Deployed AI Engineer, or in a comparable client‑embedded 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) 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 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 LLM applications with authoritative 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 with 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 teams on engineering standards, technical problem‑solving, solution delivery, and client‑facing responsibilities.
Strong client communication and consulting skills across 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, evaluate emerging technologies quickly, 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
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
Preferred Qualifications
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 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 including 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 approaches that improve enterprise RAG quality and reliability.
Experience developing reusable AI accelerators, reference architectures, engineering frameworks, or platform capabilities.
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.
Compensation and Location
Location: McLean, VA (onsite)
Salary range: USD
149,000
to
248,000
per year
Travel and Clearance
Travel required: up to
10%
Clearance required: ability to obtain Public Trust
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
Job Family Data Science Consulting
What Would Be Nice to Have Bachelor’s degree from an accredited university; Master’s degree in a related technical field; Federal Government or other regulated‑industry delivery experience; cloud and enterprise AI platform deployment experience (Microsoft Azure/Azure OpenAI, AWS/Amazon Bedrock, Google Cloud/Vertex AI, Databricks or comparable); experience with modern agent development frameworks (LangGraph, LangChain, Semantic Kernel or comparable); operationalizing generative AI (evaluation, tracing, observability, guardrails, cost optimization, production monitoring); secure AI architecture experience (IAM, secrets management, API security, data protection, authorization, auditability, responsible AI controls); container and CI/CD experience (Docker, Kubernetes, GitHub Actions, Azure DevOps, Terraform); advanced retrieval techniques and enterprise RAG quality improvements; experience building reusable AI accelerators and engineering frameworks; contributions to technical strategy and client pursuit activities; relevant certifications; and active Federal Public Trust, Secret, or higher‑level clearance.
What We Offer Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package designed to support a diverse and supportive workplace.
#J-18808-Ljbffr
Role Overview
Lead forward deployed engineering engagements with direct client partnership.
Design, prototype, test, and deploy generative AI and agentic AI applications in complex enterprise environments.
Mentor engineers and strengthen client‑facing delivery capability across forward deployed teams.
Key Responsibilities
Lead forward deployed engineering engagements by understanding complex workflows, identifying high‑value opportunities, and translating business and mission requirements into working AI solutions.
Rapidly design, prototype, test, and deploy generative AI and agentic AI applications in complex enterprise settings.
Build advanced AI solutions using large language models, APIs, developer tooling, and enterprise AI platforms.
Convert ambiguous and evolving requirements into 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.
Facilitate technical discovery, architecture discussions, demonstrations, and hands‑on solution working sessions with client stakeholders.
Partner with end users during implementation to gather feedback, diagnose challenges, refine solutions, and support adoption.
Apply emerging AI capabilities to complex client challenges while balancing usability, performance, security, maintainability, and responsible AI.
Architect, build, and maintain integrations across 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 to enterprise tools, services, and data sources.
Design and implement A2A and multi‑agent patterns that enable specialized agents to coordinate as part of 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 from rapid prototyping 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 issues across applications, models, agents, APIs, data, and infrastructure.
Establish reusable technical patterns and accelerators to improve implementation quality and speed.
Act as a trusted senior technical advisor for complex generative AI and agentic AI initiatives.
Build relationships with technical, business, data, and leadership stakeholders and communicate complex concepts, options, risks, and trade‑offs clearly.
Mentor and develop junior and mid‑level engineers into effective Forward Deployed Engineers, including coaching on discovery, decomposition, requirements translation, prototyping, solution design, client communication, demonstrations, troubleshooting, and knowledge transfer.
Provide hands‑on technical direction, architecture guidance, code review, pairing, and engineering mentorship.
Develop reusable forward deployed engineering playbooks, standards, solution patterns, accelerators, and implementation best practices.
Coach team members on 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 forward deployed practices and delivery quality.
Required Qualifications
Must be able to obtain and maintain a Federal PUBLIC TRUST. Active Public Trust or suitability is 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 as a Forward Deployed Engineer, Forward Deployed Software Engineer, Forward Deployed AI Engineer, or in a comparable client‑embedded 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) 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 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 LLM applications with authoritative 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 with 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 teams on engineering standards, technical problem‑solving, solution delivery, and client‑facing responsibilities.
Strong client communication and consulting skills across 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, evaluate emerging technologies quickly, 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
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
Preferred Qualifications
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 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 including 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 approaches that improve enterprise RAG quality and reliability.
Experience developing reusable AI accelerators, reference architectures, engineering frameworks, or platform capabilities.
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.
Compensation and Location
Location: McLean, VA (onsite)
Salary range: USD
149,000
to
248,000
per year
Travel and Clearance
Travel required: up to
10%
Clearance required: ability to obtain Public Trust
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
Job Family Data Science Consulting
What Would Be Nice to Have Bachelor’s degree from an accredited university; Master’s degree in a related technical field; Federal Government or other regulated‑industry delivery experience; cloud and enterprise AI platform deployment experience (Microsoft Azure/Azure OpenAI, AWS/Amazon Bedrock, Google Cloud/Vertex AI, Databricks or comparable); experience with modern agent development frameworks (LangGraph, LangChain, Semantic Kernel or comparable); operationalizing generative AI (evaluation, tracing, observability, guardrails, cost optimization, production monitoring); secure AI architecture experience (IAM, secrets management, API security, data protection, authorization, auditability, responsible AI controls); container and CI/CD experience (Docker, Kubernetes, GitHub Actions, Azure DevOps, Terraform); advanced retrieval techniques and enterprise RAG quality improvements; experience building reusable AI accelerators and engineering frameworks; contributions to technical strategy and client pursuit activities; relevant certifications; and active Federal Public Trust, Secret, or higher‑level clearance.
What We Offer Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package designed to support a diverse and supportive workplace.
#J-18808-Ljbffr