AI Software Engineer

1. ROLE DETAILS

Title: AI Engineer

Department: Service Delivery

Reports to: AI Solutions Architect – Lead

Direct reports: None

Location: Denver or Grand Junction, CO (hybrid-first); Remote for the right candidate

FLSA status: Exempt

Effective date: 28OCT2026

2. MISSION

EggNest.ai is moving from bespoke, project-based connector development and delivery toward repeatable, self-serve connectors, custom actions and managed integrations. This role exists to build and harden the integrations and internal platform that make that shift possible, and to help Service Delivery implement those integrations for customers on time and with fewer rework cycles. The company gets reusable platform IP, faster customer go-lives and a stronger engineering bench that can carry delivery work without relying on a single lead.

3. KEY RESPONSIBILITIES

1. Builds and maintains custom connectors and integrations between customer systems of record and the Glean platform (and other AI platforms), including source analysis, data mapping, authentication, permissions, incremental sync and deletion handling.
2. Develops and improves EggNest's internal platform (customer portal, scheduling and time-tracking features, internal tools, shared connector libraries and templates) to reduce delivery effort and support self-serve connector deployment.
3. Assists Service Delivery with integration implementation: joins customer working sessions as the technical owner, supports UAT, prepares handoffs, and helps secure customer credentials, test access and written connector acceptance early in the engagement.
4. Owns monitoring, troubleshooting and root-cause analysis for the connectors and platform components they ship; escalates and resolves production issues, API changes and customer-reported defects.
5. Documents each integration (field mappings, API coverage, known limitations, deployment steps) and contributes reusable patterns so simpler connectors can be deployed without custom engineering.
6. Follows and helps tighten engineering process: GitHub issue and branch conventions, code review, the weekly staging (Friday) and production (Monday) release cadence, AWS access and secret-handling practices, and CI/CD.

4. KEY OUTCOMES

1. Ships assigned connectors and integrations to production with customer acceptance. Outcome measured by first assigned integration live and accepted within 90 days of hire. System of record: GitHub / Jira, connector acceptance form
2. Delivers platform and internal-tool features on the weekly release cadence. Outcome measured by committed sprint items released on the weekly cadence. System of record: GitHub, sprint board
3. Keeps shipped integrations reliable. Measured by connector sync success rate of 95– 99% and critical defects resolved within 5 business days, reviewed quarterly. System of record: Monitoring dashboards, issue tracker
4. Increases reuse of connector and platform components Outcome measured by at least 2 reusable templates, libraries or deployment patterns contributed by end of month 6; documentation updated within 5 business days of each release. System of record: GitHub, internal docs

5. QUALIFICATIONS

Required

• 3–5 years of professional experience as a software, backend or integration engineer building API-driven integrations between enterprise systems (2–3 years acceptable for strong candidates).
• Strong proficiency in Python and/or JavaScript/TypeScript.
• Hands-on experience with REST APIs, including pagination, rate limiting, webhooks, error handling and retry logic.
• Working knowledge of authentication and authorization patterns (OAuth 2.0, API keys, service accounts) and permission mapping.
• Experience with cloud services on AWS or GCP (deployment, secrets, basic observability) and with Git-based workflows, code review and CI/CD.
• Familiarity with infrastructure as code, containers and secrets management; SQL and datawarehouse experience.
• Implementation experience: has taken a product or integration from kickoff through customer UAT, handoff, training and written acceptance in a professional services, customer success or implementation role.
• Strong debugging skills and clear written and verbal communication, including with nonengineering and customer stakeholders.

Preferred

• Scrum and agile delivery: comfortable contributing to sprint planning, backlog grooming, standups, retros and weekly release trains; Scrum or agile certification (CSM, PSM, PMI-ACP) is a plus.
• Project management: experience owning timelines, scope, risks and dependencies for small integration projects; PMP, CAPM or equivalent is a plus.
• Scoping and estimation: can turn an ambiguous customer request into a staged plan with effort estimates, assumptions and API feasibility checks.
• Experience with CRM, ERP, customer-success or data-platform APIs (for example HubSpot, DealCloud, Vitally, NetSuite, QuickBooks, Snowflake) and with iPaaS or orchestration tools (Workato, Boomi, MuleSoft, Zapier).
• Experience with AI-assisted development tools (Codex, Claude Code) and agent workflow design, with sound judgment on when to trust and verify generated code.
• Technical writing and customer-facing documentation experience, including runbooks and handoff packages.
• Experience mentoring junior engineers or interns, and comfort working in a small, fast-moving team where processes are still being formalized.

6. COMPETENCIES

Job-Specific Competencies: Technical

• Python - Writes clean, typed, idiomatic Python for API clients, data transformations, connectors and small services; structures code into reusable modules, handles pagination, retries and rate limits, and writes pytest unit and integration tests that run in CI.
• GitHub and version control - Works in short-lived feature branches using EggNest naming conventions for dev, prod and customer branches; writes clear commits and pull requests; reviews others' code constructively; links PRs to issues and epics; uses GitHub Actions or equivalent for CI/CD; never commits secrets.
• API and integration design - Reads vendor API documentation critically; designs source-to-target mappings; implements full and incremental sync, deletions, webhooks and idempotent retries; anticipates versioning, deprecations and schema changes.
• Authentication, permissions and security - Implements OAuth 2.0, API keys and service accounts correctly; maps source permissions so users see only what they are allowed to see; applies least-privilege AWS roles and proper secret handling.
• Testing, debugging and observability - Reproduces defects, isolates root cause with logs and metrics, adds alerts and health checks to what they ship, and verifies fixes in staging before the Monday production release.
• Cloud and deployment - Deploys and operates services on AWS (and GCP where needed) with repeatable, documented steps; contributes to the path toward 1-click customer deployments.
• Data modeling and SQL - Models source objects into the target index or database schema, validates data with SQL, and checks data quality before and after sync.
• AI-assisted engineering - Uses AI coding tools to speed up boilerplate, tests and documentation while personally owning design, security, permissions and review of the result.

Job-Specific Competencies: Behavioral

• Ownership and delivery discipline Takes an integration from scoping through UAT, handoff and written acceptance; follows release, branching and issue-tracking conventions without reminders; keeps tickets and hours current.
• Customer and stakeholder communication Explains integration behavior, limits and trade-offs in plain language to delivery leads and customers, and raises risks and blockers early.
• Reusability mindset Leaves behind documentation, templates and patterns that make the next connector faster to build, whoever builds it.

7. WORK ENVIRONMENT

Hybrid-first, based in Denver or Grand Junction, CO, working closely with the AI Solutions Architect – Lead, other connector and platform engineers, and Service Delivery leads. Occasional travel for customer or team working sessions. Standard office and computer-based physical requirements.

8. COMPENSATION & BENEFITS

• Pay range: Aligned to Market
• Variable pay: Employee Equity Plan
• Benefits: Medical, dental and vision; 401(k); paid time off; paid holidays; FSA/HSA
• Application deadline: Applications accepted on an ongoing basis

Submit your resume and relevant project or portfolio links via the EggNest.ai Careers page