Were looking for an AI Builder to serve as the technical backbone of Nomas internal AI function. Youll build the LLM-powered agents, automation pipelines, integrations, and infrastructure that teams across the company rely on every day.

This is a rare opportunity to build a new function from the ground up within a company where the work can have an immediate and meaningful impact. Youll create systems such as tools that help Sales track competitive deals in real time, pipelines that automatically turn customer calls into Salesforce updates, and agents that identify competitor activity and deliver actionable insights directly to Slack.

This is a highly hands-on role with true end-to-end ownership-from translating business needs into focused technical solutions to deploying, monitoring, and continuously improving production systems.

What Youll Do:

Build AI-Powered Agents and Automations
Design and build LLM-powered agents, automation pipelines, and backend services used across the organization.
Develop complex integrations that bring together data from multiple internal and external systems.
Build custom MCP servers and other infrastructure required to support scalable internal AI workflows.
Turn fuzzy business needs into practical, focused, and maintainable technical solutions.

Own the Internal AI Infrastructure
Harden and maintain Nomas existing MCP Gateway ecosystem.
Improve reliability, error handling, authentication, secrets management, and versioning.
Implement secure, vault-based credential management and strong security practices across production automations.
Build infrastructure that enables agents and workflows to operate reliably at scale.

Drive Production Readiness and Reliability
Own the deployment, monitoring, maintenance, and reliability of agents and automations in production.
Build evaluation frameworks, test sets, and monitoring processes to measure accuracy, precision, recall, hallucination rates, and overall agent performance.
Identify regressions and continuously improve the quality and stability of production workflows.
Troubleshoot failures across integrations, mod

Required GTM AI Engineer
About the role:
Our Revenue Operations team has already begun experimenting with AI agents and automation across the go-to-market lifecycle. Weve seen enough to know there is a much bigger opportunity.
Now were looking for an AI builder to take that from experimentation to a real capability across the Go To Market teams.
Youll identify high-value opportunities, build and deploy AI-powered workflows and agents, create the foundations that allow them to operate safely in production, and help define how AI becomes part of the everyday operating model for our GTM organization.
This is a hands-on builder role with broad ownership. Youll work across Sales, Solutions, Customer Success, Marketing and RevOps, combining engineering ability with a strong understanding of how commercial organizations actually work.
What you'll do:
Build AI into the GTM lifecycle- Design and ship agents, automations and AI-powered workflows across areas such as prospecting and enrichment, lead management, account planning, deal support, approvals, pipeline management, forecasting, call intelligence and expansion.
Turn prototypes into production systems- Take promising internal experiments and build them into reliable services that can be used across the organization. Youll work with our engineering and DevOps teams to make sure what we build is secure, maintainable and scalable.
Design for trust and appropriate autonomy- Define where AI can act independently, where people should remain in the loop, and how those boundaries are enforced. Youll build the evaluation, monitoring, permissions and audibility needed for agents that interact with real commercial systems and data.
Build on strong data foundations- Agents are only as useful as the systems and data they can rely on. Youll work across our CRM, GTM tools and data warehouse to improve how information is connected, structured and made available to AI-powered workflows.
Measure real business impact- We care less about the number of agents shipped than what they change. Youll measure impact through outcomes such as faster cycle times, better data quality, higher productivity, improved conversion or time returned to teams - and communicate that impact clearly to the business.
Make the whole organization more capable- You wont be the only person building with AI. Part of your role is creating the patterns, tooling and standards that allow others to build safely and effectively - while making it easy for the wider organization to discover, trust and use what has been created.