AI Engineer

• Own enterprise AI architecture and governance for the EHR platform
• Define reference patterns and solution blueprints for LLM applications, RAG, classical ML, and AI-enabled services
• Lead platform and tooling decisions for Azure AI, Databricks, and data orchestration
• Create reusable accelerators such as a RAG starter kit, evaluation harness, and prompt library
• Embed HIPAA-aligned PHI/PII safeguards into the SDLC and CI/CD gates
• Set responsible AI release criteria, including bias and fairness evaluations, model cards, and red-teaming
• Run the AI architecture review process
• Partner with security and compliance on risk assessments and policy updates
• Advise engineering teams on prompt design, grounding strategies, and legacy code modernization
• Mentor developers on Azure AI Foundry and secure use of Microsoft Copilot and GitHub Copilot
• Publish architecture decision records

AI Architect

• Architect and implement D365 F&SCM solutions with embedded AI, automation, and predictive capabilities
• Advise CFOs and senior leaders on AI-enabled ERP strategy
• Deliver thought leadership on AI, analytics, and automation to improve close cycles, forecasting, and operational efficiency
• Translate business requirements into scalable D365 F&SCM designs
• Integrate Power Platform and Azure AI services
• Lead cross-functional teams through finance transformation initiatives
• Drive innovation, user enablement, and adoption of AI-powered tools
• Oversee end-to-end implementations, including design validation, deployment, and post-go-live optimization
• Participate in sales pursuits by leading discoveries, configuring software demonstrations, building project plans and proposals, and leading client presentations
• Guide C-level stakeholders and align technology with business objectives

MS Dynamics 365 F&SCM Finance Architect – AI-Driven

• Architect and implement D365 F&SCM solutions with embedded AI, automation, and predictive capabilities
• Advise CFOs and senior leaders on AI-enabled ERP strategy
• Deliver thought leadership on AI, analytics, and automation
• Translate business requirements into scalable D365 F&SCM designs
• Integrate Power Platform and Azure AI services
• Lead cross-functional teams through finance transformation initiatives
• Drive innovation, user enablement, and adoption of AI-powered tools
• Oversee end-to-end implementations
• Ensure strategic alignment, design validation, deployment, and post-go-live optimization
• Participate in sales pursuits through discovery leadership, software demonstrations, project plans, proposals, and client presentations
• Provide project and engagement management, practice development, mentorship, and administrative support

• Design, ship, and operate production-grade AI systems from prototype to production
• Report to the Director of Engineering and partner with the AI Solution Architect, AI Verification Architect, and AI First Product team
• Turn product specifications and Agile artifacts into AI agents, LLM-powered features, and automation
• Integrate AI systems with JSSI enterprise systems and own their reliability, accuracy, and business impact in production
• Drive refinement of the AI agentic pipeline and iterative development loop
• Direct the design, build, and delivery of production-grade AI features and agents across JSSI's SaaS platforms
• Implement and integrate AI tools, agents, agent skills, and services across specification, development, review, testing, documentation, and release
• Build multi-agent workflows for orchestration, reasoning, planning, autonomous task execution, queues, caching, and scalable APIs
• Translate Agile artifacts into agent-ready specifications and build and consume MCP servers exposing JSSI enterprise systems as model-ready tools
• Use Claude Code to develop, review, test, and document software in tight iterative cycles, with a goal of 100% AI-generated code
• Steward the context store, coding standards, patterns, domain knowledge, and project memory
• Derive tests from acceptance criteria and make specifications executable for verification
• Build, operate, and maintain agent-based automation coordinating LLMs, tools, APIs, and enterprise data including Fabric, Salesforce, D365 F&O, and JSSI proprietary platforms
• Own agent reliability, observability, fallback behavior, and lifecycle management in production
• Direct coding agents to build internal and external applications and services such as dashboards and microservices
• Create and refine AI prompts, agent skills, and context packages; monitor, troubleshoot, and optimize automation
• Partner with stakeholders to identify and prioritize high-impact automation opportunities
• Collaborate with architects, engineering peers, product teams, citizen developers, and business stakeholders
• Follow responsible AI practices including fairness, explainability, model monitoring, ethics, and regulatory alignment

• Build and operate production AI systems improving Hyatt’s search, personalization, guest experiences, colleague productivity, and operational workflows
• Design, prototype, and productionize Generative AI solutions in natural-language search, information retrieval, and recommender systems
• Build and evaluate LLM-powered applications using retrieval-augmented generation, prompt engineering, fine-tuning, embeddings, semantic search, and agentic or workflow-based AI systems
• Develop model evaluation frameworks covering offline metrics, human evaluation, guardrail testing, bias and safety checks, and business-impact measurement
• Translate ambiguous business problems into data science problem statements, solution designs, success metrics, and implementation plans
• Lead solution design, modeling decisions, experimentation strategy, and technical tradeoff discussions
• Partner with ML and data engineering teams to deploy scalable real-time inference pipelines and batch processing workflows
• Influence technical roadmaps and sequence data science initiatives
• Collaborate on design reviews, code reviews, ML engineering best practices, and knowledge sharing
• Productionize models and AI services using AWS-native tools and modern MLOps practices
• Contribute to data pipelines, feature workflows, model serving, observability, monitoring, and lifecycle management
• Apply version control, CI/CD, testing, reproducibility, containerization, and documentation
• Support batch and low-latency inference deployments
• Work with product owners, ML scientists, ML engineers, data engineers, architects, and business stakeholders to deliver end-to-end algorithmic products
• Communicate model behavior, limitations, assumptions, risks, and business impact to technical and non-technical audiences
• Define measurable success criteria and evaluate AI outcomes
• Champion responsible AI, inclusive design, and practical experimentation

Senior AI Engineer – Search, Personalization, Agents

• Define and execute delivery roadmaps for applications using Claude Code Enterprise and agentic development
• Embed AI across the delivery cycle, including spec-driven intent, agent execution, automated verification, and production insight
• Maintain accountability for the reliability, accuracy, and business impact of shipped software, AI agents, LLM-powered features, and automation
• Align engineering standards, roadmaps, reusable patterns, and architecture recommendations with peer leaders
• Prototype, review examples, challenge assumptions, and apply hands-on engineering judgment
• Manage hiring, onboarding, performance management, and career development for a team of three to eight AI solution architects, engineers, and verification architects
• Operate a unified delivery model across onshore and offshore teams
• Coach solution architects and build team capability through learning paths, enablement sessions, office hours, and communities of practice
• Drive responsible enterprise adoption of Claude Code through onboarding, usage patterns, guardrails, training, and coaching
• Create reusable engineering assets, prompt libraries, MCP and context-sharing patterns, testing patterns, API standards, architecture decision records, and secure coding standards
• Establish AI First governance covering code ownership, reviews, IP and secrets protection, auditability, model usage, and human accountability
• Ensure responsible AI practices covering fairness, explainability, monitoring, security, and regulatory alignment
• Integrate AI systems and automation with Salesforce, Dynamics 365 F&O, Microsoft Fabric, and proprietary products
• Standardize MCP server development and consumption for enterprise systems
• Establish observability and lifecycle-management standards for production automations
• Define and report engineering, AI adoption, productivity, quality, reliability, and cost metrics
• Build relationships across Engineering, Product, Architecture, Security, Infrastructure, Data, Finance, Sales, and Operations
• Evaluate AI engineering capabilities and vendors for JSSI’s Azure/Microsoft environment and enterprise risk posture
• Communicate progress, risks, investment needs, adoption, and business impact to executives
• Partner with Product and business stakeholders to deliver on time, on target, and on budget

Director of AI Engineering

• Build and operate backend systems that serve AI-powered insurance workflows in production
• Design and implement AI orchestration layers connecting models, APIs, workflows, and business logic
• Build inference pipelines for LLM-based and AI-assisted automation systems
• Optimize latency, throughput, and cost across AI services through caching, batching, streaming, and routing
• Design stable service boundaries between backend systems, ML components, and product APIs
• Implement observability through logging, metrics, tracing, alerting, and incident response workflows
• Debug production issues across distributed AI systems and resolve root causes
• Collaborate with frontend, product, operations, and ML teams to ship end-to-end features
• Continuously improve system reliability, scalability, and performance
• Deliver reliable, low-latency, highly available AI backend systems at scale
• Ensure AI workflows are stable, observable, and production-ready across multiple products
• Improve system performance through real-world feedback and optimization
• Detect, diagnose, and resolve production incidents quickly
• Integrate AI capabilities into global customer and internal workflows

AI Backend Engineer, Artificial Intelligence

• Build and maintain production-grade Android applications using Kotlin
• Design and implement intuitive, responsive mobile interfaces for insurance, payments, and customer workflows
• Integrate backend APIs for quotations, policy management, claims, and transactional services
• Contribute to mobile features incorporating AI-assisted capabilities, including chat experiences, recommendations, and automation flows
• Optimize application performance, responsiveness, battery usage, and memory efficiency across devices
• Implement analytics, crash reporting, and user behavior tracking
• Collaborate with backend, product, design, and AI/ML teams throughout the development lifecycle
• Ensure stability, scalability, and security across production mobile systems
• Improve engineering standards, architecture, and mobile best practices
• Deliver stable, high-performance Android experiences used at scale
• Reduce crashes and production incidents through better engineering practices
• Ship mobile features with strong system design and testing discipline
• Enable AI-assisted product capabilities through mobile integration
• Contribute to scalable and maintainable mobile architecture

Android Software Engineer, Artificial Intelligence

• Build AI-powered product features integrated into real insurance workflows
• Design and optimize LLM-based interactions across customer and internal systems
• Develop prompts, reasoning flows, and structured AI outputs for production use cases
• Evaluate model performance using real-world data, feedback loops, and experiments
• Improve AI output reliability through guardrails, fallback logic, and validation layers
• Work with backend engineers to integrate AI capabilities into APIs and workflows
• Design and implement AI-driven automation features such as chat, recommendations, and decision support
• Analyze user behavior and model responses to continuously improve AI performance
• Collaborate with product, design, and engineering teams to ship end-to-end AI features
• Contribute to AI system design decisions focused on usability and real-world impact
• Deliver reliable, consistent, production-ready AI features across workflows
• Improve user-facing AI interactions and task completion rates
• Ensure model outputs are measurable, testable, and continuously improving
• Reduce manual work in insurance processes through AI-driven automation
• Enable product teams to safely deploy AI features with controlled behavior

Applied AI Engineer, Artificial Intelligence, Work From Home

• Build and ship iOS features using Swift
• Create clean mobile flows for insurance, payments, claims, renewals and financial services
• Work with product and design to simplify complex user journeys
• Integrate backend APIs and ensure mobile flows are stable and reliable
• Improve performance, crash rate, loading states, responsiveness, memory usage and battery efficiency
• Use analytics, user behavior and production issues to improve the product
• Build AI-assisted mobile experiences where they improve the user journey

AI Engineer jobs in Illinois

AI Architect

MS Dynamics 365 F&SCM Finance Architect – AI-Driven

MS Dynamics 365 F&SCM Finance Architect – AI-Driven

AI Engineer

Senior AI Engineer – Search, Personalization, Agents

Director of AI Engineering

AI Backend Engineer, Artificial Intelligence

Android Software Engineer, Artificial Intelligence

Applied AI Engineer, Artificial Intelligence, Work From Home

iOS Software Engineer, Artificial Intelligence, Remote