GenAI Architect

Job Description: Gen AI Architect

Location: Chicago, IL (Hybrid)

About the Role

We are hiring a Gen AI Architect to lead the design and delivery of production AI systems across data, risk, operations, and reporting workflows. You will define strategy, set technical direction, and work hands-on with engineering, data, and business teams to ship reliable, explainable AI capabilities.

This role balances innovation with governance: models must be accurate, auditable, and trusted by the people who act on their outputs.

Key Responsibilities

• Define AI strategy, roadmap, and reference architecture for Gen AI and ML use cases
• Design end-to-end solutions: data pipelines, model selection, orchestration, integration, and deployment
• Build LLM-based applications (RAG, agents, structured outputs) and traditional ML (ranking, anomaly detection, classification)
• Implement data quality monitoring, model evaluation, and observability
• Design human-in-the-loop workflows where users review, approve, or override AI recommendations
• Establish explainability standards, audit trails, and responsible AI practices
• Partner with stakeholders to prioritize use cases based on value, feasibility, and data readiness
• Mentor engineers and data scientists; guide build vs. buy decisions

Required Qualifications

• 8+ years in software or data architecture; 3+ years shipping AI/ML or Gen AI in production
• Strong experience with LLM applications, prompt engineering, and evaluation frameworks
• Background in ML for ranking, anomaly detection, and NLP/text analysis
• Experience with cloud AI/ML platforms and MLOps/LLMOps practices
• Proven ability to design explainable, governable AI in regulated or high-stakes environments
• Excellent communication with technical and non-technical stakeholders
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience

Preferred Qualifications

• Experience in risk, compliance, audit, or operational monitoring domains
• Knowledge of adaptive modeling, benchmarking, and feedback-driven learning
• Cloud architecture or AI/ML certifications
• Master’s degree in AI, ML, or related field

What Success Looks Like

• Clear AI roadmap aligned to business priorities
• Production Gen AI and ML capabilities delivering measurable value
• Trusted outputs with explainability and human oversight built in
• Scalable platform patterns that support ongoing iteration and improvement