Director Python Ai Jobs in Chicago, IL (NOW HIRING)

Essential Duties & Responsibilities The System Architect (SA) Director has overall technical authority for one or more programs/Agile Release Trains (ARTs) within a portfolio, with direct management accountability for a staff of Engineers and Business Analysts. The SA Director works closely with an Enterprise Architect, Product Management and Release Train Engineer (RTE) to ensure ongoing alignment of technical and business outcomes. The SA Director owns the creation, maintenance and communication of the Architecture, along with implementation by one or more development teams. The SA Director is an expert and champion of Scaled Agile Framework (SAFe) architectural design and development, incrementally building the associated mindset and behaviors across stakeholders with which they work. Owns and evolves architecture and execution roadmap, converting business problems into scalable, secure AI solutions and reusable patterns. Leads hands‐on delivery of AI solutions, partnering directly with engineers and business teams from prototype to pilot and production. Architects and implements agentic solutions (single/multi‐agent workflows) with safe autonomy. Implements rigorous evaluation frameworks for LLMs/agents and drives continuous quality improvement. Demonstrates exceptional ability to build and nurture relationships across a diverse set of stakeholders. Architects knowledge‐grounding systems to improve accuracy, traceability, and audit readiness. Accelerates engineering throughput using agentic coding workflows (e.g., Claude Code) to automate routine engineering tasks while ensuring changes are reviewed and verified via standard SDLC controls. Enables Claude Code safely in enterprise environments, defining usage guardrails for file edits/command execution, secrets handling, and auditing. Influences PI planning and backlog by defining architectural outcomes and Enabler Epics; contributes expert input to WSJF prioritization to balance value, feasibility, risk, and time‐to‐impact. Drives measurable adoption and delivery velocity, removing blockers, standardizing accelerators/templates for copilots and agents, and enabling reuse across teams. Builds and leads high‐performing AI innovation talent, hiring and developing hands‐on builders, coaching technical leadership, and sustaining an innovative pipeline. Reporting Relationship: Typically, Director or above. Skills, Knowledge & Abilities Systems architecture expertise across distributed systems, cloud‐native patterns, API/service‐based design, event‐driven architecture, and scalable workflow solutions. Applied AI / GenAI depth including LLM solution patterns, prompt engineering, model selection/routing, and production integration with enterprise apps and data. Agentic system design capability for multi‐step automation (single/multi‐agent), including orchestration, state/memory patterns, tool integration, and safe autonomy controls (permissions, approvals, auditability). Ontology and semantic layer proficiency—canonical entity/relationship modeling, business rules, and semantic consistency to improve AI grounding, reasoning, and explainability. Knowledge‐grounding and retrieval expertise—vector search, hybrid retrieval, reranking, knowledge‐graph patterns, citations/traceability, and measurable accuracy improvements. AIOps engineering strength—versioning, CI/CD for AI systems, automated eval gates, monitoring/observability, reliability, and cost/performance optimization. Responsible AI, security, and compliance‐by‐design—privacy protections, secrets handling, access controls, leakage prevention, audit logging, and model risk governance aligned to enterprise standards. Strong SDLC and hands‐on engineering skills—coding best practices, design/code reviews, pull request quality controls, testing strategies, refactoring, and operational readiness. Scaled Agile (SAFe) leadership—decomposing strategy into Enabler Epics, shaping PI outcomes, influencing WSJF prioritization, and coaching teams/ARTs to deliver with speed and quality. Exceptional leadership and communication—ability to influence cross‐functionally, negotiate tradeoffs, mentor technical talent, and drive an innovation pipeline from experimentation to measurable business impact. Education & Experience A bachelor's degree in computer science, Information Technology, Engineering, or an equivalent field is required. A master's degree in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline is preferred. Experienced in delivery, consulting and regulated domains (insurance/financial services) is preferred. 10+ years in software engineering and technical delivery, including ownership of large‐scale, distributed enterprise systems and end‐to‐end SDLC. Strong hands‐on background in Python, AI, and cloud platforms—including GCP with GCP Agent platform and Gemini. 5+ years in system/solution architecture, applying frameworks and producing architectural artifacts, reference architecture, and technical guardrails. 5+ years of people leadership, managing and mentoring teams. Compensation In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In District of Columbia, California, Colorado, Connecticut, Illinois, Maryland, Massachusetts, New York and Washington, the national base pay range for this job level is $97,000 to $189,000 annually. Salary determinations are based on various factors, including but not limited to relevant work experience, skills, certifications and location. Benefits CNA offers a comprehensive and competitive benefits package to help our employees – and their family members – achieve their physical, financial, emotional and social wellbeing goals. #J-18808-Ljbffr