Senior Manager AI/ML Engineering (Flex)

UPS Supply Chain Solutions•US - UPS CORPORATE OFFICES (GACOR), MD

6d•$128,172 - $272,736•Hybrid

About The Position

UPS is seeking a Senior Manager AI/ML Engineering to lead the design, development, and production delivery of enterprise AI applications incorporating Generative AI, agentic AI, cloud-native technologies, and enterprise systems. The role supports an enterprise business intelligence platform that delivers domain AI agents across finance and HR, including payroll, benefits, labor contracts, and financial planning. These agents are coordinated through cross-domain orchestration and an ontology-driven knowledge graph. The position requires engineering and architecture fundamentals, hands-on experience building AI-powered solutions, and judgment about when to use AI versus deterministic workflows, APIs, rules, or traditional application patterns. The role leads AI/ML engineering across frontend, backend, AI and agent layers, orchestration, integrations, data, and cloud services, while mentoring AI/ML and software engineers. It defines reusable architecture patterns, secure multi-persona architectures, enterprise integrations, data and knowledge architectures, and non-functional requirements. It also establishes standards for AI observability, evaluation, responsible AI, operational readiness, CI/CD, infrastructure as code, DevSecOps, and production deployment. The position is hybrid and requires candidates to work in a UPS IT office in New Jersey, Atlanta, Maryland, or Kentucky three days a week. The position is permanent. The stated base pay range is $128,172 to $272,736 per year, with possible eligibility for annual incentive compensation programs. Applicants must meet the stated U.S. work-authorization requirement.

Requirements

Bachelor’s degree in computer science, engineering, or a related technical field.
12+ years of experience designing and delivering enterprise applications, including significant hands-on experience building and deploying AI/ML or LLM-based solutions in production and leading engineering teams.
Strong experience with distributed systems, APIs, microservices, event-driven architecture, data persistence, state management, and enterprise integration patterns.
Hands-on experience building solutions using Generative AI, LLMs, agentic AI, RAG, and tool-calling architectures; strong Python skills and experience in prompt engineering, embeddings, and LLM evaluation.
Experience with LangGraph and/or equivalent agent orchestration frameworks, including stateful workflow and multi-agent patterns.
Experience building cloud-native AI solutions on GCP, including enterprise data platforms such as BigQuery and Databricks.
Strong experience integrating applications with enterprise APIs, transactional systems, and legacy platforms.
Experience with enterprise security patterns, including identity, authentication, authorization, service identity, secrets management, and least privilege.
Experience with production observability, distributed tracing, resiliency, performance engineering, and operational readiness.
Ability to develop prototypes and reference implementations to validate architectural decisions.
Strong communication and stakeholder-management skills, including translating complex business requirements into scalable technical solutions.
Must be a U.S. citizen or national, an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer.

Nice To Haves

Master's degree in computer science, artificial intelligence, data engineering, or a related discipline.
Experience with MCP, LangFuse, OpenTelemetry, Grafana, Vertex AI, Gemini, IBM watsonx, OpenAI, or Anthropic ecosystems.
Experience with semantic and knowledge technologies such as Stardog, Neo4j, RDF, OWL, or SPARQL, including ontology modeling and federated knowledge graph delivery.
Experience with vector search, embeddings, semantic retrieval, and enterprise knowledge platforms.
Knowledge of Responsible AI, AI governance, LLMOps, and MLOps.
Experience designing reusable enterprise AI capabilities or supporting large-scale application modernization.
Domain experience with finance and HR data such as payroll, benefits, labor contracts, or financial planning and reporting.
Relevant cloud, AI, security, or architecture certifications.

Responsibilities

Lead end-to-end AI/ML engineering across frontend, backend, AI/agent layers, orchestration, integrations, data, and cloud services; mentor AI/ML and software engineers.
Define scalable, reusable architecture patterns for multiple business capabilities, personas, and workflows.
Select between deterministic workflows, APIs, rules, AI agents, and hybrid approaches based on reliability, performance, explainability, cost, and maintainability.
Architect stateful and long-running workflows, including session and context management, checkpoints, retries, idempotency, human-in-the-loop interactions, and recovery patterns.
Design and build agentic solutions using LangGraph, LangChain, RAG, APIs, MCP, and other orchestration patterns, including semantic routing, coordinator/supervisor agents, and cross-domain multi-agent orchestration.
Deliver domain AI agents for areas such as payroll, benefits, labor contracts, financial planning, and competitive positioning, supporting cross-domain questions, scenario modeling, and financial analysis.
Define secure multi-persona architectures, including authentication, authorization, entitlements, data-access boundaries, and service identities.
Architect integrations with enterprise APIs, transactional and legacy systems, SaaS platforms, and data services using REST APIs, Apigee/API gateways, MCP, messaging, and event-driven patterns.
Define application data, semantic retrieval, vector search, and knowledge architecture patterns across relational, graph, vector, and search technologies, including ontology design and federated knowledge graphs.
Define non-functional requirements for scalability, availability, performance, resiliency, security, maintainability, recoverability, and cost efficiency.
Design cloud-native architectures on Google Cloud Platform using services such as Vertex AI, GKE, Cloud Run, AlloyDB, BigQuery, Pub/Sub, and Eventarc.
Establish AI observability, distributed tracing, LLM evaluation, accuracy benchmarking, monitoring, Responsible AI, and operational-readiness patterns, including test-question sets, UAT support, defect triage, and production release gates.
Define reference architectures, engineering standards, architecture decision records, and reusable patterns; lead hands-on implementation and code reviews.
Evaluate architecture alternatives through prototypes and proof-of-concepts and communicate technical trade-offs to engineering, product, security, platform, and business stakeholders.
Guide CI/CD, Infrastructure as Code, containerization, DevSecOps, and production deployment patterns.

Benefits

Medical/prescription drug coverage
Dental coverage
Vision coverage
Flexible Spending Account
Health Savings Account
Dependent Care Flexible Spending Account
Basic and Supplemental Life Insurance and Accidental Death and Dismemberment
Disability Income Protection Plan
Employee Assistance Program
401(k) retirement program
Vacation
Paid holidays and personal time
Paid sick and family and medical leave time as required by law
Discounted Employee Stock Purchase Program
Possible eligibility for annual short-term and/or long-term incentive compensation programs