1028 Full Stack Software Engineer Applied Ai jobs in Chicago,IL

Ulta Beauty

Bolingbrook, IL 60440

Master's degree in Computer Science, Data Science, AI/ML, or equivalent professional experience is required.
12+ years of experience in software engineering and systems architecture, with at least 5+ years specifically focused on leading large-scale AI production environments.
Experience managing other architects or technical leads (Data Scientists) and overseeing multiple simultaneous workstreams/pods.
Deep expertise in the modern AI stack, including LLM orchestration (LangChain, LlamaIndex), vector databases (Pinecone, Milvus), and traditional ML frameworks (TensorFlow, Scikit-Learn).
Hands-on experience designing agentic systems, autonomous workflows, or cognitive architectures
Strong understanding of Platform Architecture and MLOps, including Kubernetes, model serving (BentoML, TGI, vLLM), and automated retraining pipelines.
Experience, working with and influencing, Enterprise Architecture frameworks and navigating complex governance, risk, and compliance (GRC) environments.
Hands-on experience with one or more cloud computing platforms (e.g. Google Cloud, Azure, AWS)
Exceptional verbal and written communication skills, with the ability to create clear architectural diagrams and whitepapers for both technical and non-technical audiences. Comfort and experience presenting and compiling materials for Executive audiences.
Stay current with emerging AI technologies and methodologies, evaluating their potential application

Uber AI-Solutions

Cicero, IL 60804

Ulta Beauty

Bolingbrook, IL 60440

Bachelor's degree in computer science, a related field, or applicable work experience.
10+ years of experience in software development or architecture; minimum of 3+ years in a leadership role
Deep knowledge of AI/ML ecosystems
Experience designing MLOps pipelines and AI Ops frameworks at enterprise scale
Strong understanding of cloud-native architecture (AWS, Azure, Databricks or GCP), MLOps frameworks, and CI/CD principles
Experience with multi-agent architecture concepts: orchestration patterns, tool/skill registry, memory and state management, and agent observability
Experience setting technical standards and coordinating across distributed engineering teams
Proven ability to lead cross-functional engineering teams and deliver enterprise-scale AI solutions.
Comfortable navigating new, novel technology solutions and managing ambiguity to deliver in evolving domains
Familiarity with security and compliance standards for platform operations.
Strong analytical and problem-solving skills with a data-driven mindset.
Excellent communication, project management, and stakeholder engagement skills.
Comfortable with presenting up to senior leadership (VP+ level); able to present technical concepts to non-technical and executive levels