Lead AI/ML Engineering (Flex)

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This position provides technical and delivery leadership for teams developing enterprise AI, analytics, and intelligent software solutions. The role leads the design, development, integration, and production deployment of capabilities that combine generative AI, machine learning, enterprise data, analytics, and agentic technologies to solve complex business problems and accelerate decision-making.

The position partners across Business, Product, Architecture, Engineering, Data Science, and Data Engineering to translate business needs into scalable, secure, and reusable solutions. The role establishes engineering patterns and best practices for AI orchestration, data integration, evaluation, testing, observability, security, and production operations.

This position serves as a technical lead and subject matter expert, drives adoption of modern AI and software engineering practices, leads complex initiatives from concept through production, and mentors engineering talent.

• →Leads the design and implementation of enterprise AI, analytics, and intelligent software solutions across cloud and distributed environments.
• →Translates complex business problems into scalable solutions using generative AI, agentic AI, machine learning, analytics, and automation.
• →Leads development of AI agents and intelligent applications, including LLM integration, orchestration, retrieval-augmented generation (RAG), semantic retrieval, enterprise data, and tool integration.
• →Establishes reusable architectures, frameworks, engineering patterns, and components that accelerate delivery across business domains and use cases.
• →Collaborates across Business, Product, Architecture, Engineering, Data Science, Data Engineering, UX, and Security to deliver enterprise AI capabilities.
• →Leads integration of structured and unstructured enterprise data into AI, analytics, and machine learning solutions.
• →Evaluates and applies appropriate AI and ML technologies, balancing generative AI, predictive modeling, traditional machine learning, and deterministic approaches based on business requirements.
• →Establishes AI evaluation and testing practices for grounding, accuracy, relevance, reliability, performance, regression testing, and production monitoring.
• →Leads adoption of cloud-native architectures, APIs, data pipelines, and integration patterns supporting scalable AI and analytics workloads.
• →Champions responsible and secure AI practices, including data governance, identity and access, privacy, traceability, auditability, and appropriate human oversight.
• →Drives engineering excellence through CI/CD, DevSecOps, MLOps/LLMOps, automated testing, observability, deployment automation, and production operations.
• →Leads complex technical initiatives, manages dependencies and risks, resolves technical roadblocks, and provides technical guidance across project teams.
• →Partners with business and product stakeholders to prioritize capabilities based on business value, scalability, reuse, and measurable outcomes.
• →Acts as a technical leader and mentor, developing engineering talent and promoting modern AI and software engineering practices.

• 8+ years of experience across the Software Development Life Cycle (SDLC) designing, building, deploying, and operating enterprise software, data, analytics, or AI solutions.
• Demonstrated experience leading the development and production deployment of generative AI, agentic AI, machine learning, or intelligent applications.
• Experience with LLMs, RAG, AI agents, orchestration, prompt engineering, semantic retrieval, and AI evaluation.
• Experience designing scalable cloud-native architectures, APIs, data pipelines, and enterprise integrations supporting AI solutions.
• Strong understanding of AI engineering practices, including grounding, evaluation, observability, performance, security, responsible AI, and MLOps/LLMOps.
• Experience applying ML and analytical techniques such as NLP, classification, regression, forecasting, and predictive modeling.
• Experience with Agile development, CI/CD, DevSecOps, automated testing, and production operations.
• Demonstrated ability to establish reusable engineering patterns, lead complex technical initiatives, mentor engineering talent, and collaborate across technical and business teams.
• Strong written and verbal communication skills with the ability to translate complex technical concepts into business outcomes.
• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a related technical discipline, international equivalent, or equivalent job experience.

• Job Grade: 303

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

Employer will sponsor visas for specific positions. UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.