Top AI Engineer Jobs in Chicago, IL

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before contributing to client data and AI solutions. Responsibilities include collaborating across teams, solving technical problems, analyzing issues, making recommendations, managing priorities, and delivering projects. The role requires foundational AI/ML knowledge, Python proficiency, familiarity with generative AI tools, vector stores, cloud platforms, containers, MLOps, and strong communication skills.

Develop and operate scalable AI inference solutions for scientific workflows on HPC systems and AI accelerators. Build Python REST services, integrate AI models, optimize inference performance, and support distributed workloads involving batching, scheduling, memory management, and observability. Collaborate with scientific application teams, academic and industry partners, national laboratories, and agencies to advance AI-enabled scientific computing.

Designs, builds, and operationalizes secure, scalable generative AI and machine learning systems across AWS and GCP. Responsibilities include developing LLM, agentic, and RAG applications; architecting serverless, event-driven microservices; managing model experimentation, evaluation, deployment, and monitoring; and optimizing performance and cost. The role also leads responsible AI, HIPAA compliance, governance, and technical collaboration while mentoring engineers and managing team performance.

Build and operate production AI applications and shared platform capabilities using AWS Bedrock and Python. Develop AI agents, MCP servers, APIs, and distributed services; integrate AI into existing workflows; evaluate retrieval, tool-use, and orchestration approaches; and improve reliability, security, observability, and maintainability. Partner with technical and business stakeholders to identify opportunities, guide technical decisions, support delivery planning, and mentor less experienced engineers.

Build and maintain production LLM features, including RAG systems, prompt pipelines, agent workflows, and AI services. Develop Python or TypeScript backend APIs, evaluation frameworks, monitoring, deployment automation, and MLOps tooling. Ensure HIPAA-compliant handling of health data while participating in architecture, code reviews, and engineering standards. The role requires autonomous work in a greenfield environment and collaboration on clinical AI applications.

Design and lead backend platform services, REST and GraphQL APIs, integrations, security patterns, HIPAA-compliant PHI controls, AI/LLM integration, observability, and reliability infrastructure. Contribute to end-to-end full-stack features using TypeScript, Next.js, and React. Establish architecture standards and reviews while supporting scalable services used by healthcare teams and patients.

Lead end-to-end architecture and production operation of AI systems, including LLM clinical tools, RAG pipelines, agent workflows, and intelligent automation. Evaluate and fine-tune foundation models, build model and agent evaluation frameworks, and ensure safety, accuracy, clinical relevance, HIPAA compliance, and responsible AI governance. Partner with product, design, clinical, and data teams to translate care-delivery workflows into impactful AI features.

Build and deploy full-stack AI applications on Google Cloud. Develop Python and FastAPI services, PostgreSQL data models, React and TypeScript interfaces, and Vertex AI agent workflows using Gemini and ADK. Deploy containerized services to Cloud Run, manage Cloud Storage, implement retrieval with Vertex AI Search, write automated tests, document solutions, and collaborate with delivery teams and customer stakeholders.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Design, develop, test, operate, and maintain AI-enabled software and infrastructure. Build production-ready code, RAG pipelines, vector search integrations, and agentic AI components. Follow SRE/DevOps and responsible-AI practices, participate in code reviews, troubleshoot model quality, and collaborate across engineering, product, and business teams to deliver scalable GenAI solutions.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Lead design and deliver SAP BTP AI solutions for Finance. Partner with SAP Finance teams to identify AI use cases, build GenAI capabilities (RAG, Joule Skills, agents), ensure secure, scalable integrations, validate feasibility via POCs, and produce production-ready solutions for reconciliations, controls, reporting, and financial close.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Build and enhance enterprise AI platform services spanning AIOps, MLOps, FMOps, and LLMOps. Develop GenAI and ML capabilities across Google Cloud, AWS, and Azure; architect scalable solutions; implement monitoring, CI/CD, testing, and serverless applications; and promote engineering best practices. The role also involves collaborating across data science, business, and cloud teams, mentoring engineers, reviewing system designs, and advancing RAG, vector search, agentic frameworks, and responsible AI capabilities.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Build and operate scalable data pipelines, evaluation frameworks, and production AI/ML/NLP models. Own the full lifecycle from data collection and transformation through training, deployment, monitoring, and continuous improvement. Develop robust tooling, observability, and quality checks while collaborating with product and engineering to integrate AI capabilities into user-facing features. Work pragmatically in a fast-moving startup environment and translate business needs into effective data and machine learning solutions.

Leads enterprise AI adoption by evaluating vendors, models, platforms, and emerging technologies; designing reusable agentic frameworks and reference architectures; developing prototypes and proof of concepts; and guiding production-ready AI implementations. Partners with business, engineering, security, legal, compliance, and procurement teams to establish AI decision frameworks, integration standards, guardrails, and scalable engineering patterns while advising senior leaders on investments and tradeoffs.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Develop and deploy scalable AI and machine learning applications within the OneStream platform. Responsibilities include building software with Python and SQL/NoSQL, troubleshooting full-stack systems, designing complex interfaces, collaborating cross-functionally, and applying data science solutions to products. The role focuses on technologies such as forecasting, anomaly detection, and natural language processing, with opportunities to use cloud platforms, APIs, databases, and machine learning frameworks.

Designs, develops, and deploys enterprise Generative AI platforms using LLMs, RAG, agentic architectures, and modern orchestration frameworks. Builds Python microservices and AI workflows across Azure and AWS, while implementing GenAIOps, containerization, infrastructure as code, CI/CD, monitoring, security, governance, and compliance. Partners with engineering, platform, architecture, security, and product teams to deliver scalable, resilient, production-ready AI solutions.

Develop and deploy scalable AI and machine learning applications, including forecasting, anomaly detection, and natural language processing solutions. Design software architectures, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role requires production machine learning experience, strong programming skills, and the ability to learn evolving AI technologies. Preferred experience includes web service frameworks, distributed systems, and Azure cloud computing.