Senior Manager AI/ML Engineering

UPS

. Lead end-to-end AI/ML engineering across frontend, backend, AI/agent layers, orchestration, integrations, data, and cloud services .

Posted 10/31/2026full-timeUnited StatesSenior💰 $128,172 - $272,736 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI/ML engineering, including the design and implementation of scalable architectures and agentic solutions. Proficient in cloud-native development on GCP and integrating enterprise applications with a focus on security and performance.

Highest-signal resume keywords

AI/ML EngineeringCloud-Native Development on GCPEnterprise Application IntegrationTeam LeadershipGenerative AI and LLMs

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonDistributed SystemsAPIsMicroservicesEvent-Driven ArchitectureData PersistenceState ManagementLangGraphSemantic RetrievalVector Search

Soft Skills

Strong CommunicationStakeholder Management

Tools & Technologies

Vertex AIBigQueryDatabricksMCPLangFuseOpenTelemetryGrafanaIBM WatsonxOpenAIAnthropic

Certifications & Qualifications

Relevant Cloud CertificationsAI CertificationsSecurity CertificationsArchitecture Certifications

Industry Keywords

Finance DataHR DataResponsible AIAI GovernanceMLOps

Tech Stack

Tools & technologies

BigQueryCloudDistributed SystemsGoogle Cloud PlatformGrafanaMicroservicesNeo4jPython

About the role

Key responsibilities & impact

Lead end-to-end AI/ML engineering across frontend, backend, AI/agent layers, orchestration, integrations, data, and cloud services
Mentor a team of AI/ML and software engineers
Define scalable, reusable architecture patterns for multiple business capabilities, personas, and workflows
Determine appropriate use of deterministic workflows, APIs, rules, AI agents, and hybrid approaches
Architect stateful and long-running workflows with session/context management, checkpoints, retries, idempotency, human-in-the-loop interactions, and recovery patterns
Design and build agentic solutions using LangGraph, LangChain, RAG, APIs, MCP, semantic routing, coordinator/supervisor agents, and cross-domain multi-agent orchestration
Deliver domain AI agents for payroll, benefits, labor contracts, financial planning, and competitive positioning
Define secure multi-persona architectures with authentication, authorization, entitlements, data-access boundaries, and service identities
Architect integrations with enterprise APIs, transactional and legacy systems, SaaS platforms, and data services
Define application data, semantic retrieval, vector search, and knowledge architecture patterns, 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 GCP using Vertex AI, GKE, Cloud Run, AlloyDB, BigQuery, Pub/Sub, and Eventarc
Establish AI observability, distributed tracing, LLM evaluation, accuracy benchmarking, monitoring, Responsible AI, UAT support, defect triage, and production release gates
Define reference architectures, engineering standards, ADRs, 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 stakeholders
Guide CI/CD, Infrastructure as Code, containerization, DevSecOps, and production deployment patterns

Requirements

What you’ll need

Bachelor’s degree in computer science, Engineering, or a related technical field
12+ years of experience designing and delivering enterprise applications
Significant hands-on experience building and deploying AI/ML or LLM-based solutions in production
Experience leading engineering teams
Strong experience with distributed systems, APIs, microservices, event-driven architecture, data persistence, state management, and enterprise integration patterns
Hands-on experience with Generative AI, LLMs, agentic AI, RAG, tool-calling architectures, Python, 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 BigQuery and Databricks
Strong experience integrating applications with enterprise APIs, transactional systems, and legacy platforms
Experience with 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
Strong communication and stakeholder-management skills
Preferred: Master’s degree in computer science, Artificial Intelligence, Data Engineering, or a related discipline
Preferred: Experience with MCP, LangFuse, OpenTelemetry, Grafana, Vertex AI, Gemini, IBM watsonx, OpenAI, or Anthropic ecosystems
Preferred: Experience with semantic and knowledge technologies such as Stardog, Neo4j, RDF, OWL, or SPARQL
Preferred: Experience with vector search, embeddings, semantic retrieval, and enterprise knowledge platforms
Preferred: Knowledge of Responsible AI, AI governance, LLMOps, and MLOps
Preferred: Experience designing reusable enterprise AI capabilities or supporting large-scale application modernization
Preferred: Domain experience with finance and HR data
Preferred: Relevant cloud, AI, security, or architecture certifications
Must be a U.S. Citizen or National of the U.S., an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer

Benefits

Comp & perks

Annual short-term and/or long-term incentive compensation programs may be available
Medical/prescription drug coverage
Dental coverage
Vision coverage
Flexible Spending Account
Health Savings Account
Dependent Care Flexible Spending Account
Basic and Supplemental Life Insurance & 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
Hybrid work arrangement