AI Application Engineer – Backend & LLM Integration

Role overview

Key responsibilities

• Develop backend services and APIs using Python and frameworks such as FastAPI or Flask.
• Integrate LLMs into project management workflows using suitable commercial APIs or open-source models.
• Implement AI capabilities to generate project status reports, meeting minutes, action items, executive summaries, and stakeholder updates.
• Build workflows to extract information from project documents, emails, spreadsheets, meeting transcripts, and status reports.
• Develop AI-powered task summarization, action-item tracking, dependency identification, and project query-answering capabilities.
• Implement tool-calling and agentic workflows to execute controlled, multi-step business processes.
• Build integrations with project management and enterprise systems through REST APIs and webhooks.
• Develop data validation, structured output generation, exception handling, and human-approval workflows.
• Work with databases to store project information, AI-generated outputs, workflow status, and audit logs.
• Implement authentication, authorization, role-based access, and appropriate safeguards for AI operations.
• Develop automated tests for API endpoints, prompts, AI workflows, and integration scenarios.
• Monitor application performance, troubleshoot production issues, and optimize response time and LLM usage costs.
• Collaborate with the AI Architect and full-stack engineer to deliver complete, tested features.

Required technical skills

• Strong Python programming and object-oriented programming fundamentals.
• Experience developing backend applications and RESTful APIs.
• Hands-on experience integrating LLM APIs into software applications.
• Familiarity with prompt engineering, structured outputs, function calling, and tool calling.
• Experience with at least one framework such as LangChain, LlamaIndex, or an equivalent.
• Knowledge of SQL databases, preferably PostgreSQL, and database design.
• Understanding of JSON, API authentication, asynchronous processing, and error handling.
• Familiarity with Git, Docker, automated testing, and CI/CD practices.
• Understanding of application security, access control, and secure handling of business data.
• Ability to debug AI outputs and implement validation and fallback mechanisms.

Preferred qualifications

• Experience developing RAG-based applications and integrating vector databases.
• Experience with AI agents, workflow orchestration, or automation frameworks.
• Familiarity with document parsing, OCR, speech-to-text, or meeting transcript processing.
• Experience integrating Jira, ServiceNow, Microsoft Teams, SharePoint, or similar systems.
• Exposure to cloud platforms such as AWS, Azure, or Google Cloud.
• Experience working on enterprise workflow automation, SaaS applications, or project management products.
• Bachelor's degree in Computer Science, Information Technology, or a related discipline.

Key deliverables

• Backend APIs supporting AI-powered project management features.
• Automated meeting minutes, action items, and project reporting workflows.
• LLM integrations with structured outputs and error-handling mechanisms.
• Enterprise system integrations and controlled AI workflow execution.
• Automated tests, API documentation, and deployment-ready application services.

Success criteria

• Deliver reliable and maintainable AI application features.
• Reduce manual effort in preparing meeting minutes, project reports, and action trackers.
• Ensure AI-generated outputs follow defined validation and quality standards.
• Maintain secure data handling, access controls, and auditability.
• Deliver well-tested integrations and APIs within agreed development timelines.