About the job
This position is part of the Collins Digital Technology AI organization, supporting the company's mission to develop and scale AI capabilities responsibly, securely, and strategically across the enterprise. The AI Solution Architect serves as a trusted technical advisor to internal business customers across Collins, guiding the identification, evaluation, and implementation of AI-enabled Enterprise AI solutions.
Responsibilities
Use Case Evaluation & Solution AdvisoryPartner with internal business customers across Collins strategic business units and central functions to understand AI use case requirements, assess feasibility, and translate business needs into solution options with clearly articulated tradeoffs.
Evaluate use cases against the existing enterprise AI capability portfolio, recommending proven internal platforms, tools, and services to accelerate delivery and avoid redundant development.
Assess commercially available vendor AI tools, platforms, and services as candidate solutions; conduct structured capability evaluations including technical fit, security posture, integration complexity, data governance implications, cost modeling, and vendor viability.
Enterprise AI Strategy & GovernanceDrive alignment of proposed AI initiatives with enterprise AI strategies, architecture standards, and technology roadmaps to avoid proliferation of overlapping capabilities and fragmented investments.
Champion the use of enterprise and tools as the preferred delivery path before recommending third-party or custom development.
Maintain awareness of the enterprise AI capability inventory and assist in communicating available solutions to internal customers to accelerate adoption.
Responsible AI & Risk ManagementApply RTX's Responsible AI framework to evaluate proposed use cases and solutions, identifying risks related to bias, fairness, transparency, explainability, human oversight, and data privacy.
Assess vendor AI product documentation, AI transparency reports, and contractual representations to validate claims and identify gaps or misrepresentations.
Identify technical, operational, ethical, regulatory, and reputational risks in proposed AI initiatives; develop risk findings and recommend mitigations, conditions of approval, or denial where appropriate.
Stakeholder Engagement & CommunicationServe as a point of contact and trusted consultant for strategic business unit AI leads, program offices, and functional leaders seeking guidance on AI adoption strategies.
Communicate complex technical concepts, tradeoffs, and risk findings clearly to audiences including senior leadership, program managers, engineers, and non-technical business stakeholders.
Qualifications
Minimum
Typically requires a University Degree and minimum 10 years prior relevant experience or an Advanced Degree in a related field and minimum 7 years of experience
U.S. Citizenship is required. The ability to obtain and maintain a U.S. government issued security clearance may be required for certain assignments.
Familiarity with enterprise AI/ML architecture patterns, including generative AI, large language models (LLMs), retrieval-augmented generation (RAG), ML pipelines, and model governance.
Experience working across multiple business functions or programs as a technical advisor, solutions architect, or enterprise architect with a track record of translating business requirements into actionable technical recommendations.
Demonstrated knowledge of AI risk, ethics, and responsible AI principles, including experience applying governance frameworks in a practical decision-making context.
Strong written and verbal communication skills with a proven ability to synthesize complex technical information into clear, concise recommendations for senior stakeholders.
Preferred
Experience in an Aerospace and Defense or regulated government contracting environment, with familiarity with export control (EAR/ITAR), data classification, and program security requirements as they relate to AI systems.
Knowledge of the NIST AI Risk Management Framework (AI RMF), DoD AI Ethics Principles, or equivalent responsible AI frameworks.
Hands-on experience building, deploying, or operating AI/ML solutions, including working with cloud AI services (Azure AI, AWS Bedrock, Google Vertex AI) and enterprise AI platforms.
Experience evaluating AI vendor statements of work, capability roadmaps, and AI transparency documentation; familiarity with SaaS/AI contractual risk provisions.
Understanding of enterprise architecture governance processes and technology portfolio management, including experience contributing to an architecture review board or similar governance body.
Advanced degree (M.S. or Ph.D.) in Computer Science, Artificial Intelligence, Systems Engineering, Data Science, or a related technical field.
Relevant professional certifications such as AWS/Azure/GCP AI certifications, TOGAF, SABSA, or Certified AI Governance Professional (CAIGP).
Prior experience with digital transformation programs, technology rationalization, or enterprise IT/AI platform consolidation initiatives.