AI Agent Engineer – Commercial AI Transformation

1 мес. назад

CanadaWorldwideMiddle

version controltestingrelease practicesdata warehouseenterprise integration

Develop and deploy AI agents that automate commercial business processes with software engineering discipline in an enterprise environment.

О компании

is the AI leader in governance, risk and compliance (GRC) SaaS solutions, helping more than 1 million users and 700,000 board members to clarify risk and elevate governance. The One Platform gives practitioners, the C-Suite and the board a consolidated view of their entire GRC practice so they can more effectively manage risk, build greater resilience and make better decisions, faster. 
Learn more at .com or follow us on  LinkedIn  and  Facebook
 
 
What Offers You  
Creativity is ingrained in our culture. We are innovative collaborators by nature. We thrive in exploring how things can be differently both in our internal processes and to help our clients
We care about our people.  offers a flexible work environment, global days of service, comprehensive health benefits, meeting free days, generous time off policy and wellness programs to name a few
We have teams all over the world . We may be headquartered in New York City, but we have office hubs in Washington D.C., Vancouver, London, Galway, Budapest, Munich, Bengaluru, Singapore, and Sydney.
Diversity is important to us. Growing, maintaining and promoting a diverse team is a top priority for us. We foster and encourage diversity through our Employee Resource Groups and provide access to resources and education to support the education of our team, facilitate dialogue, and foster understanding.
created the modern governance movement. Our world-changing idea is to empower leaders with the technology, insights and connections they need to drive greater impact and accountability – to lead with purpose. Our employees are passionate, smart, and creative people who not only want to help build the software company of the future, but who want to make the world a more sustainable, equitable and better place. 
Headquartered in New York, has offices in Washington D.C.,  London, Galway, Budapest, Vancouver, Bengaluru, Munich, Singapore and Sydney.  To foster strong collaboration and connection, this role will follow a hybrid work model. If you are within a commuting distance to one of our office locations, you will be expected to  work onsite at least 50% of the time.  We believe that in-person engagement helps drive innovation, teamwork, and a strong sense of community.
 
We are a drug free workplace. is proud to be an equal opportunity employer. We do not discriminate based on race, color, religious creed, sex, national origin, ancestry, citizenship status, pregnancy, childbirth, physical disability, mental disability, age, military status, protected veteran status, marital status, registered domestic partner or civil union status, gender (including sex stereotyping and gender identity or expression), medical condition (including, but not limited to, cancer related or HIV/AIDS related), genetic information, or sexual orientation in accordance with applicable federal, state and local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also 's EEO Policy and Know You
At , we recognize the value of a diverse global workforce. Visa sponsorship may be available for select positions based on business needs and specific role requirements. Sponsorship decisions are evaluated on a case-by-case basis, and eligibility should not be assumed for all opportunities.
To all recruitment agencies: does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees or any other organization location. is not responsible for any fees related to unsolicited resumes.

Обязанности

A growing, reliable portfolio of agents in production, each automating a real, meaningful volume of work
The team's agent-building work has real version control, testing, and release discipline behind it, not just working prototypes
PoCs still move at PoC speed. Rigor shows up where it matters (production) not everywhere
Commercial stakeholders trust that agents reflect how their work actually happens
Pipelines and agents respect existing access control and governance boundaries without needing to be told twice

Требования

Bachelor's degree in Computer Science, AI/ML, or a related field
4+ years building software, including demonstrated experience automating business processes at meaningful scale (not one-off scripts)
Strong software development lifecycle (SDLC) fundamentals: version control (Git), code review practices, testing, and release/deployment discipline
Hands-on experience building integrations or data pipelines using an iPaaS/automation platform (e.g., Workato, Boomi, Mulesoft, or similar)
Experience working with a cloud data warehouse (e.g., Snowflake) for data ingestion, transformation, or processing
Recent hands-on experience designing, building, and deploying LLM-based agents or agentic workflows into production
Working understanding of enterprise identity and access concepts (SSO, OAuth, group-based permissions) and how they constrain what a pipeline or agent can access
Demonstrated judgment about when to move fast and informal (PoC stage) versus when to apply full engineering rigor (production stage)
Genuine interest in and some exposure to how a commercial org (sales, CS, or ops) functions

Будет плюсом

Experience with enterprise search or knowledge platforms (e.g., Glean) and how they scope and surface indexed content
Familiarity with Microsoft 365 ecosystem tooling relevant to data governance (e.g., Purview, Defender, Graph API)
Experience working with SIEM or logging platforms (e.g., Panther, Splunk) from an integration or engineering standpoint
Experience in a presales, customer success, or commercial operations environment
Familiarity with Salesforce or similar commercial data systems
Experience mentoring or upskilling less traditionally-trained engineers on SDLC best practices
Test-Driven Development experience

Другое

's Commercial AI Transformation function builds AI agents that automate real workflows across our commercial organization, from sales and customer success to internal operations. We've already proven the model with a handful of production agents (including tools that generate executive briefs, estimate costs, and calculate customer value). This role exists to grow that portfolio, working alongside a teammate who also designs and builds agents.
You'll bring two things the team needs more of: a track record of automating real business processes at volume, and genuine software engineering discipline (version control, testing, release practices) that the team can build on as it scales. At the same time, you need the agility to move fast on a proof of concept without forcing full engineering process onto something that's still proving itself out. Knowing when to apply which is part of the job.
You'll also step into a real enterprise integration environment on day one, pulling from source systems, processing through a data warehouse, feeding an enterprise search/AI index, and operating inside access control and governance boundaries that are already in place. Comfort with that kind of environment matters as much as agent-building skill itself.
Map business processes independently when needed, and design, build, and deploy AI agents and agent chains that automate them
Refine agents through iteration: tightening prompts, handling edge cases, improving reliability based on real usage
Move quickly through early-stage PoCs, then apply appropriate engineering rigor once an agent is heading toward production
Build and maintain data pipelines that pull from source systems (e.g., Microsoft Graph API, Teams, Snowflake) into a data warehouse, applying appropriate filtering, summarization, and sensitivity handling before anything is indexed or surfaced
Work within an iPaaS/integration platform (e.g., Workato) to build and maintain recipes and API endpoints that connect systems together, including logging and monitoring for those integrations
Understand how enterprise search/AI indexing tools (e.g., Glean) consume processed data, including index scoping, access restrictions by group, and how retrieval respects underlying permissions
Apply access control patterns correctly: privileged access boundaries, IP whitelisting, OAuth-based endpoint protection, and group-based restrictions on what data or tools a user can reach
Understand how identity and access (e.g., Okta/SSO) and logging/SIEM tooling (e.g., Panther) fit around the systems you're building, enough to build in a way that doesn't create gaps
Work with sensitivity tagging and data minimization principles when pulling raw data (e.g., removing what isn't needed, redacting or filtering employee-specific content) before it moves further into the pipeline
Introduce and drive adoption of solid software engineering practices across the team's agent-building work: version control, code review discipline, testing, and release/deployment practices
Set a practical bar for what "production-grade" means for an agent, distinct from what's acceptable in a fast-moving PoC, and help the team recognize which stage something is in
Own agents from prototype through production-grade deployment, including error handling, monitoring, and failure-mode recovery
Extend and reuse existing shared infrastructure rather than duplicating capability; apply an "extend, don't rebuild" discipline
Understand enough about how sales, customer success, and commercial operations actually work to design agents that reflect reality, not a theoretical process
Partner closely with the teammate who also designs and builds agents, sharing the design and build workload flexibly
Bring engineering best practices to the team without slowing down the team's pace on early-stage work