AI Engineering Productivity Lead – AgentOps & Developer Experience

Guidehouse•Tysons, VA

7d•$130,000 - $216,000

About The Position

The AI Engineering Productivity Lead designs and implements Guidehouse’s AI-native engineering delivery model, turning coding agents and GenAI tooling into repeatable, secure, and measurable workflows. This role accelerates delivery speed and improves quality across the software lifecycle by delivering shared tooling, patterns, and reusable assets adopted by delivery teams at scale.

Requirements

Bachelor’s degree from an accredited college/university
Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST"
Based on our contractual obligations, candidate must be located within the United States and US Citizen
8+ years of software engineering experience, including meaningful work in developer platforms, internal tools, platform engineering, or automation that improved engineering throughput and quality
Strong Cloud and DevOps fundamentals: CI/CD, testing strategy, code review practices, release engineering, and operational reliability
Hands-on experience implementing and operating AI-assisted engineering workflows (coding agents, agentic task execution, AI-enabled automation), including rollout strategies, quality gates, and safe adoption practices
Practical understanding of modern AI application patterns (RAG, tool use, agent workflows) and how they change failure modes, validation needs, and delivery processes
Ability to define, track, and communicate productivity outcomes using meaningful metrics and dashboards
Highly self-motivated, with the demonstrated ability to independently identify opportunities, define scope in ambiguous environments, and drive end-to-end execution with full ownership
Strong stakeholder management and change leadership in a matrixed environment, with a bias toward shipping working solutions and iterating based on data

Nice To Haves

Experience rolling out secure developer tooling in regulated environments (health, financial services, public sector)
Familiarity with observability and telemetry instrumentation applied to developer workflows and AI usage
Experience building opinionated starter kits and “paved roads” adopted across multiple teams, with documented adoption and measurable impact
Experience integrating developer tooling across IDEs, GitHub, CI/CD, knowledge systems, and collaboration tools to reduce end-to-end delivery friction
Background in platform engineering, SRE, or security engineering that informs safe-by-default agent workflows and governance

Responsibilities

Define and roll out AI-assisted development practices within existing SDLC and delivery frameworks, including how to scope work for coding agents, set clear acceptance criteria, and review AI-generated changes effectively
Coordinate with IT, Security and other teams to ensure development tool usage aligns with Enterprise guidelines.
Create and maintain starter kits, repo templates, and reference implementations for common AI delivery patterns (secure RAG, agent workflows, evaluation harnesses, enterprise integrations) with documentation and onboarding paths.
Establish safe-by-default configurations and usage standards for AI coding assistants and agent tooling (permissions models, data handling rules, sandboxing patterns, secrets handling, audit logging expectations) aligned to enterprise controls
Define instrumentation and reporting for AI-assisted delivery (cycle-time impact, defect and rework rates attributable to AI output, model and tool usage, cost and latency, policy violations) and publish actionable dashboards for leaders and delivery teams
Identify workflow friction points specifically related to AI tooling adoption (context setup, repo conventions, review burden, test gaps, security approvals), then prioritize and deliver practical fixes through automation, guidance, and reusable assets
Drive adoption through hands-on enablement: playbooks, office hours, training sessions, and curated workflow patterns so delivery teams can extend shared AI assets safely and consistently
Partner with delivery, security, and platform stakeholders to iterate on policies and paved-road patterns based on telemetry, developer feedback, and incident learnings
Stay current on emerging AI-assisted engineering and AgentOps capabilities, rapidly evaluate new tools and platform features (security, quality, cost, and developer impact), and publish clear recommendations and adoption guidance on which AI tools to standardize across the organization

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