AI Engineer

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Lead and mentor teams of data scientists and ML engineers
• Manage client relationships and promote satisfaction with deliverables
• Translate intricate business challenges into AI-driven strategies
• Design and implement AI solution architectures
• Drive innovation and excellence in client engagements
• Analyze data to derive actionable insights and solutions
• Collaborate with stakeholders to align on project objectives
• Uphold exceptional standards of quality and integrity in every task

ERP AI Engineer – Manager

• Analyzing client needs to implement and integrate business applications effectively
• Providing training and support to clients for seamless utilization of software solutions
• Leveraging analytical reasoning to optimize operational efficiency through business applications
• Collaborating with clients to understand and address their strategic objectives
• Utilizing business data analytics to inform insights and recommendations
• Managing project delivery and resource management to meet client expectations
• Implementing technology solutions to support business transformation initiatives
• Developing documentation to support application software implementation and integration
• Engaging in strategic planning to align technology solutions with business goals
• Navigating complex situations to build meaningful client relationships and inspire team members

AI Integration Developer, Senior Associate

• Build production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams.
• Design and implement production AI platform capabilities, including agents, retrieval-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
• Build an AI-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
• Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
• Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
• Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
• Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
• Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post-launch improvements.

Lead AI Engineer

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• Design, build, and maintain production grade AI and ML solutions across Azure, AWS, and other cloud platforms
• Implement end to end AI application pipelines including data ingestion, prompt engineering, evaluation, deployment, and monitoring
• Collaborate closely with engineering, data, and business teams to translate requirements into practical AI and ML implementations
• Write high quality, scalable, and maintainable code for AI and ML workloads
• Optimize performance, reliability, and cost efficiency of cloud based AI and ML services
• Integrate AI and ML solutions with enterprise data platforms such as data lakes, data warehouses, APIs, and streaming systems
• Support AI solution lifecycle management including versioning, prompt iteration, evaluation, and production support
• Apply security, compliance, and data privacy best practices in all AI and ML implementations
• Contribute to technical design discussions and provide guidance on AI and ML implementation patterns
• Stay current with advancements in AI, ML, and cloud technologies and apply them pragmatically

Senior AI/ML Engineer

• Design, build, and maintain production-grade Generative AI, Agentic AI, and machine learning applications.
• Develop evaluation and testing approaches to measure model performance, identify regressions, and improve solution quality.
• Apply LLMs, multimodal models, retrieval-augmented generation (RAG), structured extraction, and tool calling to solve real-world healthcare workflows.
• Partner with Engineering, Product, Data, and Operations teams to integrate AI capabilities into client’s systems.
• Improve the reliability, observability, guardrails, and monitoring of production AI applications.
• Stay current with advancements in AI technologies and apply them pragmatically to deliver business value.

Senior AI Engineer – Contract To Hire

• Partner with the Manager of Data Science & AI Engineering to scope and deliver AI-driven product features and internal tools.
• Design and implement machine learning and generative AI solutions using cloud services such as AWS Bedrock, SageMaker, and Amazon Q in QuickSight.
• Integrate AI services into web and application layers (e.g., via REST APIs, LangChain, or Bedrock SDK).
• Develop proof-of-concepts for natural language querying, document summarization, forecasting, and user experience enhancements using AI.
• Work with structured and unstructured data stored in AWS RDS, SQL Server, and other data sources.
• Collaborate with data engineering and analytics teams using tools like Power BI, QuickSight, and Python-based data pipelines.
• Ensure responsible AI design, including model monitoring, bias testing, and performance validation.
• Stay current with emerging technologies in AI (LLMs, vector databases, RAG architectures, and MLOps best practices).

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Develop and maintain relationships with homebuyers, Realtors, builders, financial advisors, and referral partners.
• Guide borrowers through the mortgage process from application through closing.
• Analyze financial information and recommend appropriate loan solutions.
• Build and manage a consistent referral pipeline through networking and relationship development.
• Deliver exceptional customer service throughout the lending process.
• Maintain compliance with all federal, state, and company lending guidelines.
• Grow a sustainable book of business through outstanding client experiences and referral relationships.

Licensed Mortgage Loan Originator

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Manage small team of architects performing solution engineering for the PBM organization
• Provide solution design and guidance to ensure that the solution adheres to PSS Architecture Guiding Principles and standards while maximizing reuse of technology assets.
• Actively lead and participate in the Architecture community to drive modernization opportunities for the advancement of PBM technology while keeping in mind the complexities of our IT landscape
• Providing leadership, coaching and guidance to App Dev, Technology Services and Solutions.
• Lead and drive proofs-of-concept and technology deep-dives specific to partner technologies
• Advise and assist App development teams with technical roadblocks such as performance challenges within a solution in key strategic initiatives

Lead Architect, AI, API Integration

• Manage the Enterprise Intake Process
• Ensure alignment between AI governance standards and broader enterprise policies
• Conduct trend analysis on AI use case submissions
• Create enterprise reports and risk trend summaries

AI Governance Operations Lead Director

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Enterprise AI Enablement Lead enterprise-wide AI adoption initiatives across business and technical departments.
• Evaluate business processes to identify opportunities for AI-assisted productivity improvements.
• Develop enterprise AI implementation playbooks, reusable workflow patterns, and department-specific adoption strategies.
• Assist departments in determining when Artificial Intelligence, Robotic Process Automation (RPA), workflow automation, APIs, or traditional process improvements represent the most appropriate solution.
• Promote AI as a productivity enhancement tool that augments employee capability while preserving critical business knowledge and decision-making.
• Conduct AI Opportunity Assessments with business leaders to identify repetitive work, knowledge-intensive activities, and workflow bottlenecks suitable for AI-assisted automation.
• Analyze departmental workflows and recommend practical AI solutions that improve efficiency and business outcomes.
• Design AI-enabled business workflows that improve operational effectiveness while preserving appropriate human oversight and accountability.
• Partner with department leadership to prioritize AI initiatives based on business value, operational readiness, organizational impact, and regulatory considerations.
• Facilitate workshops that help departments identify practical AI use cases and develop implementation roadmaps.
• Develop and deliver enterprise AI education programs for technical and non-technical employees.
• Deliver role-based AI enablement training tailored to departments including Development, Revenue Cycle Management, Operations, Finance, Human Resources, and Information Technology.
• Teach effective prompting, AI verification techniques, responsible AI usage, AI limitations, and methods for validating AI-generated content.
• Develop AI best practices that encourage employees to understand, validate, and appropriately apply AI-generated recommendations.
• Maintain internal AI documentation, implementation standards, approved use-case libraries, and enterprise AI knowledge resources.
• Provide ongoing AI coaching through workshops, office hours, and departmental consultations.
• Partner with Information Security, Compliance, and Legal to operationalize enterprise AI standards.
• Translate organizational policies, security standards, and regulatory requirements into practical AI implementation guidance.
• Coordinate AI implementation reviews and risk assessments with appropriate governance stakeholders.
• Assist with the development and maintenance of AI-related standards, procedures, and implementation guidance while ensuring policy ownership remains with the appropriate governance teams.
• Maintain the organization's catalog of approved AI platforms, approved AI use cases, and enterprise AI implementation guidance.
• Monitor emerging AI technologies applicable to healthcare operations and enterprise business functions.
• Evaluate new AI capabilities for business value, security implications, regulatory alignment, and operational impact.
• Measure AI adoption, productivity improvements, employee engagement, and organizational outcomes.
• Track AI adoption metrics and business value to support continuous organizational improvement. Recommend enhancements to AI-enabled business processes and enterprise AI strategy. Identify opportunities to responsibly expand AI capabilities throughout the organization.

Enterprise AI Enablement Lead

• lead the product strategy, roadmap, and execution for a multi-agent AI decision intelligence platform
• serve as the bridge between business stakeholders, data science, engineering, and operations teams to deliver scalable AI-powered solutions
• define platform capabilities, prioritize investments, and drive the development of agentic AI workflows, predictive analytics solutions, and decision intelligence products
• translate complex business challenges into product requirements, manage cross-functional delivery teams, and ensure the platform delivers measurable value

Lead Decision Scientist – AI Product

• Design, develop, and maintain backend services and APIs using modern frameworks (e.g., Java Spring Boot, Node.js, or Python FastAPI)
• Integrate and deploy Generative AI models (e.g., OpenAI, Hugging Face, LangChain) into production environments
• Fine-tune and optimize LLMs for specific use cases
• Build secure, scalable, and high-performance microservices
• Monitor and improve system performance, reliability, and scalability
• Implement CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes)
• Deploy and manage applications on cloud platforms, preferably Microsoft Azure
• Participate in code reviews, testing, and continuous integration processes

AI Software Engineer

• Partner with enterprise customers to scope and implement AI Agent use cases across complex workflows
• Integrate IFS Loops platform with customer systems
• Tune agent behavior and logic to deliver measurable outcomes (e.g., lower costs, increase in efficiency, fewer errors)
• Troubleshoot deployments and iterate quickly to ensure performance, accuracy, and user adoption
• Collaborate closely with Product, Engineering, and Customer Success to feed customer insights into the roadmap
• Document technical decisions and hand off stable deployments to customer engineering or support teams

Forward Deployed AI Engineer

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Contribute to the design of AI-enabled solution architectures within the Avanade Catalyst Solutions portfolio
• Apply and help develop reusable patterns, reference architectures, and accelerators
• Adapt pre-built AI solutions to support client needs and evolving market requirements
• Ensure solutions meet standards for scalability, security, and production readiness
• Collaborate with engineering and delivery teams to identify opportunities for AI-driven tools and methods
• Support the development of repeatable, outcome-based services
• Help drive consistency in how AI solutions are implemented across engagements
• Support architecture and deployment of ready-made AI offerings
• Partner with security, risk, and compliance teams to align with enterprise requirements
• Provide guidance to delivery teams

AI Engineer jobs in Illinois

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AI Architect

ERP AI Engineer – Manager

AI Integration Developer, Senior Associate

Lead AI Engineer

AI Legal Engineer

Senior AI/ML Engineer

Senior AI Engineer – Contract To Hire

AI Engineer

Senior Software Engineer – Applied AI

Licensed Mortgage Loan Originator

Product Engineer – AI

Lead Architect, AI, API Integration

AI Governance Operations Lead Director

AI Engineer

Enterprise AI Enablement Lead

Lead Decision Scientist – AI Product

AI Software Engineer

Forward Deployed AI Engineer

AI & Automations Lead

Manager, Global AI Enterprise Architect