The role
Move from strategy to working capability.
You will embed with customers, learn how their work really happens, and stand up AI workers inside their real systems. Senior partners set the strategy and operating roadmap. You make that roadmap real, move the system into production, and own what happens next.
What you will do
Build across the complete deployment cycle.
Discover with senior partners
Interview people, observe work, inspect systems, and find the gap between the documented process and the real one.
Design where intelligence belongs
Turn the roadmap into a build plan: which workers, connected to what, doing which steps, with which human approvals.
Build the working system
Connect AI workers to real data, knowledge, tools, evaluation suites, audit trails, and recovery paths.
Deploy into production
Move from shadow mode to increasing autonomy on top of the customer's existing systems.
Prove and expand
Monitor quality, activity, cost, and outcomes, then expand only as evidence earns it.
What we expect
Own the outcome, not the activity.
Build for the unhappy path
Exception handling is part of the product, not an afterthought.
Earn trust
Make the customer team successful while changing how the work gets done.
Use judgment
Know when not to use AI and show the return when you do.
Communicate both ways
Explain the architecture to an engineer and the outcome to a VP.
Qualifications
What you bring.
Must-have
- A graduate or post-graduate degree in computer science, engineering, information systems, data, or a related field—or equivalent demonstrated ability.
- At least one year of relevant experience in software engineering, solutions engineering, technical implementation, AI/ML, or meaningful project work.
- Strong fundamentals in Python or JavaScript/TypeScript.
- Comfort with APIs, webhooks, authentication flows, SQL, and structured and unstructured data.
- Hands-on experience building with LLMs or agent systems. You have built something that works, not only prompted a chatbot.
- A public project, portfolio, GitHub contribution, internship, research project, or production example you can explain and defend.
Nice-to-have
- Customer-facing or on-site software deployment experience.
- Experience with evaluation frameworks, retrieval, production monitoring, KPIs, or SLAs.
- Familiarity with business systems such as Salesforce, HubSpot, Microsoft 365, or Google Workspace.
- A consulting, operations, or business-analysis mindset.
Your first 30 days
Learn by building.
Master the workforce
Build a working agent for a real workflow with tools, guardrails, memory, and audit.
Make it recover
Add validation, exception handling, structured outputs, and unhappy-path behavior.
Make it measurable
Build the golden dataset and evaluation suite, tune cost, and measure impact.
Defend it like an FDE
Present the architecture and decisions, then explain the outcome to a business owner.
Logistics
How the role works.
- Location
- US-based; Dallas–Fort Worth strongly preferred.
- Working model
- Hybrid, with on-site customer work during active deployments.
- Travel
- Required based on deployment needs; expectations are confirmed before an offer.
- Authorization
- Must be authorized to work in the United States.
- Employment
- Full-time, with compensation and benefits commensurate with experience.
AI Xccelerate is an equal opportunity employer. We evaluate candidates on ability and fit for the role and welcome applicants from all backgrounds.