Your role at DynatraceAs a part of a new team focused on accelerating AI adoption and usage, the AI Engineer will design and deploy AI-powered automation, and intelligent agents embedded directly into business operations. This role operationalizes generative AI using governed enterprise data, leveraging enterprise data as the data foundation, and enterprise tools for orchestration and execution. The position partners with Data, IT, and business teams to automate workflows, support decision-making, and improve operational efficiency. The focus is on applying AI to real processes — not research or model training. This role will report up into the VP of AI, Collaboration and Data's team. Your role at Dynatrace: AI & Copilot Development - Build internal AI assistants and copilots for support, operations, and business teams - Implement Retrieval Augmented Generation (RAG) using Snowflake data and curated metrics - Ground AI responses using modeled enterprise data rather than documents alone - Implement prompt strategies, guardrails, and response evaluation techniques Workflow & Decision Automation - Automate operational processes such as ticket triage, routing, approvals, and document handling - Develop AI-driven classification, summarization, and recommendation services - Implement human-in-the-loop workflows and exception handling - Continuously improve workflows based on business outcomes and user feedback Production Engineering & Integration - Build and maintain AI-powered backend services, APIs, and microservices - Integrate AI capabilities with enterprise systems (ITSM, CRM, ERP, and internal applications) - Troubleshoot failures across data pipelines, orchestration, and model inference layers - Participate in technical design and architecture discussions Data Platform Integration - Utilize Snowflake as the trusted data source for AI decisions - Use dbt models as the semantic and business logic context for automation - Enable real-time and batch data-driven decision support - Ensure AI actions align with defined business metrics and data definitions Cloud Orchestration & Observability - Implement serverless workflows using AWS (Lambda, Step Functions, API Gateway, S3, EventBridge) - Monitor system performance, latency, and operational reliability - Track AI usage, accuracy, and cost efficiency - Implement logging, auditing, and traceability of AI decisions This is a remote eligible position. Candidates who live within a 45 mile radius of Boston, MA; Detroit, MI; and Denver, CO will be required to work hybrid (2 days per week) out of our Dyntrace office. Candidates are required to work EST hours for this position. What will help you succeedMinimum Requirements - 5+ years of software or ML engineering experience, with 2+ years of building LLM systems in production. Preferred Requirements - Expert-level proficiency in Python, plus TypeScript or Go for full-stack AI applications. - Proven ability to communicate complex AI concepts clearly to non-technical stakeholders — translating engineering trade-offs and model behavior into business terms that inform decisions. - Experience implementing production RAG systems using Snowflake Cortex Search, pgvector, hybrid search, and re-ranking strategies. - Hands-on experience building MCP (Model Context Protocol) servers and clients. - Proven track record implementing AI observability. - Experience working with LLM APIs (OpenAI, Anthropic, Azure , Gemini) and cloud platforms (AWS SageMaker, Lambda, S3, Bedrock). - Familiarity with CI/CD and MLOps tooling (MLflow, Weights & Biases, Snowflake ML Registry). - Demonstrated application of responsible AI practices on live deployments, including bias checks, output validation, and human-in-loop escalation. - A track record of proactively evaluating and introducing new AI tools or frameworks that deliver tangible improvement — not just awareness of trends but applied adoption. - Experience managing production incidents and model rollbacks in high-stakes environments. - Snowpark or external functions in Snowflake - Experience with enterprise SaaS platforms (ServiceNow, Salesforce, or similar) - Workflow orchestration tools (Airflow, n8n, or similar) - Authentication and access control concepts (OAuth, RBAC, SSO) - Exposure to vector search or semantic retrieval technologies Why you will love being a Dynatracer• A one-product software company creating real value for the largest enterprises and millions of end customers globally, striving for a world where software works perfectly. • Working with the latest technologies and at the forefront of innovation in tech on scale; but also, in other areas like marketing, design, or research. • A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries. • An environment that fosters innovation, enables creative collaboration, and allows you to grow. • A globally unique and tailor-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals. • A truly international mindset that is being shaped by the diverse personalities, expertise, and backgrounds of our global team. • A relocation team that is eager to help you start your journey to a new country, always there to support and by your side. • Attractive compensation packages and stock purchase options with numerous benefits and advantages. Compensation and RewardsDOE, salary $150K - $180K, plus Health, Dental, Life, STD, LTD, 401K, PTO. Total compensation may vary depending on candidate experience/education and location. Equal Employment OpportunityAll your information will be kept confidential according to EEO guidelines. We offer competitive compensation, company-sponsored premium benefits, medical, dental, vacation/holidays, company matching 401(k) Plan, etc. Dynatrace is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, gender identity, religion, national origin, ancestry, citizenship, physical abilities, age, sexual orientation, creed, disability status, veteran status, pregnancy, genetic status, or any other characteristic protected by law. If your disability makes it difficult for you to use this site, please contact careers@dynatrace.com. Dynatrace participates in E-Verify, participant information in English and Spanish. Right to work information in English and Spanish. EEO is the Law. To be considered for this position, please upload your resume/CV.

OVERVIEW Live the experience. From professional empowerment to continual learning opportunities. From ongoing investment in new and emerging technologies to a career of self-determination. At Ulta Beauty, our tech team is critical to our scalability—and is recognized that way. We’ve been defined as a “mature start-up.” A place where interdepartmental exposure, open doors, and genuine collaboration is ubiquitous. Where challenges come fast and furious, requiring agility, mental dexterity, and creativity. Where our passion for better solutions drives us and is core to who we are. We’re engineering for the future of retail, and it’s no-holds-barred. But for those motivated by continual change and ambiguity, by superior leadership, by whip smart colleagues who will press you daily for your very best, you’ll find that virtually nothing’s impossible at Ulta Beauty. COMPANY: Ulta Beauty Credit Services Corporation LOCATION: 1000 Remington Blvd., Suite 120, Bolingbrook, IL 60440 TITLE: Sr Artificial Intelligence/Machine Learning (AIML) Engineer HOURS: Monday to Friday, 8:00 am to 5:00 pm DUTIES: Lead the full lifecycle execution of complex AI initiatives, bridging the gap between exploratory data science and production engineering, including formulation and statistical modeling to deployment and scaling. 20.0% Develop and optimize advanced algorithms for recommendation and decision engines, utilizing techniques such as collaborative filtering, embeddings, clustering, reinforcement learning, and constrained optimization. 15.0% Design pipelines for Large Language Models (LLMs) including fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering for enterprise retail use cases like analytics augmentation and decision support. 15.0% Take ownership of the non-functional requirements of AI systems, explicitly optimizing models for low-latency inference, high throughput, data privacy compliance, and cloud cost efficiency. 10.0% Transition code from experimental notebooks to modular, version-controlled software packages. Implement automated training pipelines, CI/CD for ML, and robust monitoring for data drift and model degradation. 10.0% Contribute to and enforce engineering standards within the Enterprise AI team, including unit testing, documentation, peer code reviews, and reproducibility frameworks. 5.0% Partner with business and IT product teams to refine vague use cases into concrete engineering requirements, identifying technical risks early in the project planning process. 5.0% Map out detailed technical roadmaps, providing accurate time and effort estimates for both the research (Data Science) and implementation (Engineering) phases of projects. 5.0% Mentor junior team members and peers on both statistical best practices and software engineering principles, fostering a team culture of technical excellence. 10.0% Present technical results and architectural decisions to senior stakeholders, effectively communicating the trade-offs between model complexity, performance, and cost. 5.0% REQUIREMENTS: Master’s degree in Computer Science, Mathematics, Physics, Statistics or other quantitative field. Three (3) years in any occupation with experience working in data science, machine learning or AI.Three (3) years in any occupation with experience working in data science, machine learning or AI must include: Three (3) years of experience working in the Retail Industry. Three (3) years of experience writing production-quality object-oriented code such as Python or C++ and working with ML frameworks such as scikit-learn, TensorFlow, or similar, including three (3) years of experience building low-latency or real-time ML systems. Three (3) years of experience designing, building, and deploying large-scale recommender or decisioning systems, including techniques such as ranking, clustering, and other unsupervised learning methods. Three (3) years of experience applying optimization techniques in production environments, including large-scale ML or decisioning systems such as operational research, offer optimization, or revenue maximization. Three (3) years of experience building and deploying production ML systems on cloud platforms such as Google Cloud, Azure or AWS. Three (3) years of experience building scalable, production-grade real-time inference services using Docker, with CI/CD and monitoring. Two (2) years experience building or integrating large-scale batch ML pipelines using cloud-native event-driven systems such as Pub/Sub and containerized execution such as GKE. One (1) year of experience designing and developing reinforcement learning models in real-world or production settings. One (1) year of experience working with large language models (LLMs). Experience must also include: Experience working with large-scale data processing and analytics platforms, including Apache Spark and Databricks, for feature engineering, model training, and data pipelines; Experience with relational databases including Postgres, Bigquery and MySQL; Document databases including MongoDB; and lowlatency databases including BigTable; and Experience building and delivering complex ML solutions from inception to launch. TELECOMMUTING EMPLOYEE: Reports to company headquarters in Bolingbrook, IL. Can work remotely or telecommute up to 100%. SALARY: $165,984 - $166,984 per year The pay range for this position is $165,984.00 - $166,984.00 / Year with the opportunity for eligible associates to earn additional compensation pursuant to the Company’s bonus plan. Exact pay will be based on factors including, but not limited to relevant education, qualifications, certifications, experience, level, shift, geographic location, and business and organizational needs. Full-time positions are eligible for paid time off, health, dental, vision, life and disability benefits. Part-time positions are eligible for dental, vision, life, and disability benefits. For additional information concerning our benefits, visit our Benefits and Career Development page: https://learn.bswift.com/ulta ABOUT At Ulta Beauty (NASDAQ: ULTA), the possibilities are beautiful. Ulta Beauty is the largest North American beauty retailer and the premier beauty destination for cosmetics, fragrance, skin care products, hair care products and salon services. We bring possibilities to life through the power of beauty each and every day in our stores and online with more than 25,000 products from approximately 500 well-established and emerging beauty brands across all categories and price points, including Ulta Beauty’s own private label. Ulta Beauty also offers a full-service salon in every store featuring—hair, skin, brow, and make-up services. We will consider for employment all qualified applicants, including those with arrest records, conviction records, or other criminal histories, in a manner consistent with the requirements of any applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.

• Build AI-Powered Developer Tools: Design, implement, and ship production-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on-call assistance tools • Implement LLM Integrations: Build robust, production-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization • Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi-step workflows, tool calling, context management, and agent-to-agent communication • Contribute to Platform Infrastructure: Build self-service platform capabilities that enable teams across to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling • Drive Adoption of AI-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across engineering organization • Ensure Production Quality: Write well-tested code with strong test coverage (unit, integration, end-to-end); establish monitoring, alerting, and operational excellence for AI systems • Collaborate Cross-Functionally: Partner with Principal Engineer on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations • Participate in Operations: Take part in on-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability • Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development • Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience • On-Call Rotation: Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability

• Core Platform Development: Responsible for the development of the core agent platform components. • Architecture Transition: Lead the transition from a JavaScript prototype to a segregated Python application with dedicated Management APIs and Agent Execution Backends. • Agent Orchestration: Build and manage complex agent workflows using LangChain and LangGraph to handle multi-step interactions and tool calls. • No-Code & Pro-Code Tooling: Develop a platform that allows administrators to build agents via a "no-code" approach while enabling advanced users to execute custom code scripts. • RAG & Search Optimization: Implement advanced retrieval strategies, including hybrid search and rerankers, while justifying database choices through proof-of-concept metrics. • Guardrails & Compliance: Ensure AI safety by implementing logits masking, context managers, and content guardrails to prevent harmful or off-brand generation.