Top Tech Jobs & Startup Jobs in Chicago, IL

Manages enterprise information security governance, risk, and compliance initiatives. Develops security policies and frameworks, leads data classification and threat modeling, conducts risk assessments, oversees vulnerability remediation, evaluates security controls, supports audits, and prepares risk metrics and executive reports. Partners with technical teams to embed security requirements and promotes governance best practices across the organization.

Leads QA initiatives and teams while designing Java, Selenium, and Cucumber automation frameworks. Establishes testing strategies, quality metrics, defect processes, and CI/CD integration. Oversees functional, regression, integration, API, and end-to-end testing for AWS-hosted applications. Collaborates with development, DevOps, product, and leadership teams, mentors engineers, manages risks, and drives continuous improvements in automation coverage, testing efficiency, and release quality.

Designs, develops, and operates enterprise-grade platforms for AI agents and Generative AI applications. Responsibilities include agent orchestration, LLM and AI service integration, secure tool and API invocation, prompt lifecycle management, governance and safety controls, observability, model evaluation, deployment, troubleshooting, and platform optimization. The role collaborates with engineering, security, product, and data teams to deliver scalable, reliable, and compliant AI solutions.

Develop and maintain automated testing frameworks and suites for UI, API, integration, database, functional, regression, and end-to-end testing. Create test strategies, plans, cases, and scenarios; validate system controls and compliance requirements; manage defects; analyze quality metrics; and support release and production validation. Collaborate with product, business analysis, development, and DevOps teams throughout the software development lifecycle.

Develop responsive, accessible, and performant full-stack user interfaces using React or Vue.js, JavaScript or TypeScript, and Java backends. Responsibilities include component architecture, state management, REST API integration, CSS implementation, frontend tooling, UI testing, browser performance optimization, and CI/CD deployment processes. The developer will collaborate with backend teams and work within an Agile environment.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Develop and maintain scalable Java and Spring Boot backend applications, microservices, and RESTful APIs. Responsibilities include database integration, code reviews, performance optimization, production troubleshooting, testing, CI/CD, cloud deployment, and collaboration with product, QA, and DevOps teams.

Lead iOS and Android mobile application development. The job description does not provide further responsibilities, qualifications, or technical details. Compensation ranges from $52,000 to $182,000 annually, with benefits available to full-time employees. Independent contractor arrangements are also available.

Develop and maintain scalable Java and Spring Boot backend applications and REST APIs. Optimize CPU, memory, and overall application performance; identify bottlenecks; resolve production issues; and contribute to architecture, code reviews, reliability, and scalability. The role also involves Node.js or Python FastAPI services, cross-functional collaboration, and potential work with GraphQL, generative AI, cloud platforms, containers, CI/CD, and monitoring tools.

Design and implement enterprise AI architectures spanning GenAI, machine learning, RAG, agentic workflows, data pipelines, APIs, and production deployments. Build Python prototypes and AI services, define governance and evaluation frameworks, integrate models with enterprise platforms, and guide engineering teams through implementation. The role requires hands-on expertise with LLM applications, orchestration frameworks, model lifecycle management, monitoring, security, and scalable production AI systems, preferably in financial services.