Jira Service Management Intake Automation Developer

Jira Service Management Intake Automation Developer

Brampton, ON

Contract

We are seeking a Jira Service Management Intake Automation Developer to build scalable intake platforms that transform how teams submit, triage, prioritize, route, and track work across the enterprise.

This role combines Jira Service Management (JSM) configuration and automation, Java/Python development, API integration, and applied AI to improve request quality, operational efficiency, and decision-making. The successful candidate will own the full lifecycle of intake solutions - from requirements gathering and workflow design through implementation, deployment, monitoring, and continuous improvement.

Key Responsibilities

• Design and scale Jira Service Management portals, intake forms, request types, custom fields, workflows, approvals, queues, SLAs, permissions, and validation rules.
• Build automation to classify, prioritize, route, assign, and track work with minimal manual intervention.
• Develop reusable workflows for task creation, subtasks, dependencies, notifications, escalations, SLAs, and status transitions.
• Develop production-grade Java services, utilities, and integrations supporting Jira intake automation and enterprise workflows.
• Use Python and scripting languages to develop automation, integration utilities, AI workflows, and operational tooling.
• Develop and maintain REST APIs, webhooks, middleware, and event-driven integrations using Java, Spring Boot, Python, JSON, and HTTP.
• Apply AI and intelligent automation to classify requests, extract structured data, recommend priority and ownership, identify duplicates, and improve routing accuracy.
• Integrate Jira with Confluence, Slack, Microsoft Teams, GitHub, Azure DevOps, email, identity platforms, and other enterprise systems.
• Design responsible AI workflows incorporating validation, confidence thresholds, explainability, security, privacy, human review, and fallback mechanisms.
• Create dashboards and reports covering intake volume, demand patterns, aging, throughput, SLA performance, routing accuracy, and automation effectiveness.
• Troubleshoot production issues using logs, metrics, audit records, distributed tracing, and test cases.
• Establish testing, monitoring, deployment, rollback, access-control, security, and governance practices for Jira, Java, Python, and AI solutions.
• Partner with engineering, product, program management, QA, operations, and business teams to translate ambiguous requirements into reliable technical solutions.
• Document technical designs, business rules, AI behavior, workflows, integrations, and operational procedures.
• Monitor intake-system performance and continuously identify opportunities for automation and process improvement.

Required Qualifications & Key Skills

• 3+ years of experience administering, developing, or automating Jira Service Management or similar enterprise workflow platforms.
• Strong experience with Jira Service Management administration, configuration, workflow design, and automation.
• Strong Java/Spring Boot and/or Python application development experience.
• Hands-on experience with REST APIs, webhooks, JSON, HTTP, OAuth 2.0, and enterprise system integrations.
• Experience applying AI/GenAI, NLP, or intelligent automation to business workflows.
• Strong knowledge of SQL, secure production-system design, troubleshooting, monitoring, and observability.
• Experience with Atlassian Forge, ScriptRunner, Assets, Advanced Roadmaps, or relevant Marketplace applications.
• Knowledge of LangChain, Amazon Bedrock, RAG, embeddings, vector search, and AI evaluation is highly desirable.
• Experience with AWS or other cloud platforms.
• Knowledge of Docker, Kubernetes, Infrastructure as Code, and Configuration as Code.
• Familiarity with CI/CD pipelines and modern software delivery practices.
• Understanding of Agile methodologies and experience working in cross-functional teams.
• Strong troubleshooting, analytical, communication, and technical documentation skills.
• Understanding of responsible AI principles, including security, privacy, explainability, human oversight, and fallback mechanisms.