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GenAI / NLP Engineer MultiAgent AI Platforms (Network Planning)
Chicago, IL based, onsite 2 3 days per week (approximately 5 6 days per month minimum)
15 month contract
Overview
Client s Network Planning team within the Commercial Systems portfolio is launching a GenAI Enablement Initiative to enhance decision support, insight generation, and usability of planning data and models. This role will support the design and delivery of a scalable, cloud-based multi-agent GenAI platform integrated with United s enterprise AI/ML ecosystem (MARS).
The successful candidate will contribute to building a supervisory chatbot and specialized AI agents that deliver insights, diagnostics, workflow guidance, and automated reporting for Network Planning stakeholders. This is a net-new role supporting a growing GenAI capability.
Key Responsibilities
GenAI Platform & Architecture
Deliver initial use cases end-to-end into production, while creating a reusable foundation for future expansion:
Use Case 1: Network Planning Knowledge Assistant (Supervisory Chatbot)
Must-Haves
GenAI / NLP Engineer MultiAgent AI Platforms (Network Planning)
Chicago, IL based, onsite 2 3 days per week (approximately 5 6 days per month minimum)
15 month contract
Overview
Client s Network Planning team within the Commercial Systems portfolio is launching a GenAI Enablement Initiative to enhance decision support, insight generation, and usability of planning data and models. This role will support the design and delivery of a scalable, cloud-based multi-agent GenAI platform integrated with United s enterprise AI/ML ecosystem (MARS).
The successful candidate will contribute to building a supervisory chatbot and specialized AI agents that deliver insights, diagnostics, workflow guidance, and automated reporting for Network Planning stakeholders. This is a net-new role supporting a growing GenAI capability.
Key Responsibilities
GenAI Platform & Architecture
- Design and build an end-to-end, multi-agent GenAI architecture integrated with United s MARS platform, enterprise GenAI tooling, and existing ML infrastructure.
- Develop a supervisory/orchestrator chatbot that routes user requests to specialized downstream agents.
- Design modular, extensible AI agents to support evolving business use cases.
- Ensure response traceability, accuracy, and adherence to Responsible AI, security, and governance standards.
- Collaborate closely with Network Planning, MARS platform, Cloud Engineering, and Security teams.
Deliver initial use cases end-to-end into production, while creating a reusable foundation for future expansion:
Use Case 1: Network Planning Knowledge Assistant (Supervisory Chatbot)
- Embed an AI-powered chatbot within the Network Planning MediaWiki UI.
- Enable users to understand terminology, navigate applications, and retrieve knowledge.
- Leverage MediaWiki content as a knowledge base using NLP/LLM techniques.
- Provide contextual, explainable responses with links to source content.
- Ingest recurring airline capacity and schedule reports (e.g., PDFs).
- Generate executive-ready weekly summaries of network changes.
- Surface insights and recommendations (e.g., increase or reduce capacity, airline participation).
- Implement approval workflows prior to report distribution.
- Diagnostic analyst agents ( why did the model produce this output? ).
- What-if scenario and planning workflow agents.
- Guided parameter tuning and optimization support.
- Build required data pipelines, retrieval mechanisms, and system integrations for each agent.
- Participate in testing, validation, and production deployment using client s established MLOps and GenAI frameworks.
- Support continuous improvement, automation, and onboarding of additional agents as the ecosystem grows.
Must-Haves
- Experience architecting and building GenAI and NLP applications, ideally using multi-agent or agent-orchestrated patterns.
- Hands-on experience integrating LLMs and GenAI solutions into production enterprise environments.
- Proficiency in Python; Julia experience strongly preferred (or willingness to ramp quickly).
- Experience working with existing enterprise AI/ML platforms (non-greenfield builds).
- Strong problem-solving skills with the ability to work independently in ambiguous environments.
- Background in machine learning or ML-driven applications.
- Experience with cloud-based architectures (AWS preferred).
- Familiarity with model diagnostics, explainability, or decision-support systems.
- Highly independent, driven, and proactive.
- Strong communication skills, particularly the ability to explain GenAI concepts and outputs to non-technical stakeholders.
- For senior candidates: serve as the primary GenAI thought leader, guiding design decisions and mentoring less experienced team members.