AI Agent Engineer – Commercial AI Transformation
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Key skills for this role
About the Role
Diligent is seeking an AI Agent Engineer to build AI agents that automate workflows across sales, customer success, and operations. You will design and deploy LLM-based agents, build data pipelines, and apply software engineering discipline.
Key Skills for This Role
Responsibilities
- Map business processes independently 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 filtering, summarization, and sensitivity handling
- 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
- Understand how enterprise search/AI indexing tools (e.g., Glean) consume processed data, including index scoping and access restrictions
- Apply access control patterns correctly: privileged access boundaries, IP whitelisting, OAuth based endpoint protection, and group based restrictions
- Work with sensitivity tagging and data minimization principles when pulling raw data
- Introduce and drive adoption of solid software engineering practices across the team's agent building work: version control, code review, testing, and release/deployment
- 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
- Partner closely with the teammate who also designs and builds agents, sharing the design and build workload flexibly
Requirements
- 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
- Strong 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 pipeline/agent 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
Full Job Posting
Overview
- Diligent's Commercial AI Transformation function builds AI agents that automate real workflows across the commercial organization, from sales and customer success to internal operations.
- You'll bring a track record of automating real business processes at volume and genuine software engineering discipline, while having the agility to move fast on proofs of concept.
Key Responsibilities
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Pay Range
- CAD 120,000—CAD 150,000 CAD
Work Arrangement
- This role will follow a hybrid work model. If you are within a commuting distance to one of our Diligent office locations, you will be expected to work onsite at least 50% of the time.
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