Diligent'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). 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. 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.

Process

Automation & Agent Building Map business processes independently when needed, and design, build, and deploy AI agents and agent chains that automate them 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 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 Understand enough about how sales, customer success, and commercial operations actually work to design agents that reflect reality, not a theoretical process Bring engineering best practices to the team without slowing down the team's pace on early‑stage work The team's agent‑building work has real version control, testing, and release discipline behind it, not just working prototypes 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 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., 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 Glean) and how they scope and surface indexed content Familiarity with Microsoft 365 ecosystem tooling relevant to data governance (e.g., Experience in a presales, customer success, or commercial operations environment Familiarity with Salesforce or similar commercial data systems 120,000 – $150,000 CAD Diligent 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 Diligent 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. At Diligent, we’re building the future with people who think boldly and move fast. Whether you’re designing systems that leverage large language models or part of a team re‑imagining workflows with AI, you’ll help us unlock entirely new ways of working and thinking.

The future belongs to those who keep learning, and we are building it together. At Diligent, you’re not just building the future—you’re an agent of positive change, joining a global community on a mission to make an impact. We thrive in exploring how things can be differently both in our internal processes and to help our clients.

We care about our people. Diligent 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. Though we may be headquartered in New York City, we have office hubs in Washington D.Diversity is important to 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. Headquartered in Current York, Diligent has offices in Washington D.If you are within a commuting distance to one of our Diligent office locations, you will be expected to We believe that in‑person engagement helps drive innovation, teamwork, and a strong sense of community. 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 Diligent's EEO Policy and Know Your Rights. We are committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.

If you need assistance or an accommodation due to a disability, you may contact us at [email protected] sponsorship may be available for select positions based on business needs and specific role requirements.

📌 AI Agent Engineer - Commercial AI Transformation (Vancouver)
🏢 Diligent-14787b60
📍 Vancouver