Ardoq Developer

Key Responsibilities

• Configure and maintain Ardoq workspaces, metamodels, components, references, fields, views, surveys, and permissions.
• Model applications, business capabilities, technologies, integrations, data flows, risks, and technical debt.
• Develop Python utilities and services for data ingestion, transformation, validation, enrichment, and synchronization.
• Integrate Ardoq with systems such as ServiceNow, Azure DevOps, Jira, GitHub, SharePoint, Azure, AWS, and other enterprise platforms.
• Build and maintain REST API integrations using JSON, HTTPS, authentication tokens, and service accounts.
• Apply AI and Large Language Models to analyze enterprise data, identify technical debt, classify findings, summarize issues, and generate recommendations.
• Develop and maintain AI prompts, response schemas, validation rules, and guardrails.
• Implement scheduled imports, data mappings, reconciliation, error handling, logging, and monitoring.
• Create Ardoq dashboards, visualizations, reports, and stakeholder-specific architecture views.
• Support application portfolio management, technology lifecycle management, capability mapping, and application rationalization.
• Investigate and resolve integration, platform, access, AI-output, and data-quality issues.
• Prepare technical designs, integration specifications, operational runbooks, and user documentation.

Must-Have Skills:

• 3 to 7 years of experience in product engineering, enterprise architecture tooling, integration development, or platform engineering.
• Hands-on experience of Ardoq workspaces, metamodels, components, references, fields, visualizations, surveys, and integrations.
• Strong Python programming skills, including API clients, data processing, automation, and error handling.
• Experience with REST APIs, JSON, HTTPS, OAuth 2.0, API tokens, and integration troubleshooting.
• Experience with AI services such as Azure OpenAI or equivalent enterprise LLM platforms.
• Knowledge of Retrieval-Augmented Generation, embeddings, vector search, AI agents, and prompt evaluation.
• Experience with Python frameworks and libraries such as FastAPI, Pydantic, Pandas, and Pytest.
• Experience with data transformation, validation, mapping, synchronization, and reconciliation.
• Understanding of business, application, data, integration, and technology architecture.
• Practical knowledge of AI and LLM concepts, including prompt engineering, structured outputs, grounding, and response validation.
• Experience using Git and standard software-development practices such as code reviews, testing, and version control.
• Strong analytical, troubleshooting, documentation, and communication skills.