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Senior Manager AI/ML Engineering (Flex)
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- From $128,172 USD / year
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Closed listing

UPS

Senior Manager AI/ML Engineering (Flex)

MAHWAH, NJ, United States

From $128,172 USD / year

This opening has closed.

The original listing is kept below for reference.

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Job details

Pay: From $128,172 USD / year
Posting language: English
Posted: Oct 31, 2026

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Original job description

Archived listing. The details below describe a past opening.

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Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow - people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.

Job Description:

THIS POSITION IS HYBRID AND WILL REQUIRE CANDIDATES TO WORK IN THE FOLLOWING UPS IT LOCATIONS: NEW JERSEY, ATLANTA, MARYLAND, AND KENTUCKY OFFICE 3 DAYS A WEEK

We are seeking a highly skilled 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 . This role supports an enterprise business intelligence platform that delivers domain AI agents across finance and HR areas such as payroll, benefits, labor contracts, and financial planning , coordinated through cross-domain orchestration and an ontology-driven knowledge graph. The ideal candidate combines strong engineering and architecture fundamentals with hands-on experience building AI-powered solutions using technologies such as LangGraph, LangChain, GCP, RAG, MCP, knowledge graphs, and modern observability frameworks . This role requires the ability to determine where AI adds value and where deterministic workflows, APIs, rules, and traditional application patterns provide the better solution.

Key Responsibilities

Lead end-to-end AI/ML engineering across frontend, backend, AI/agent layers, orchestration, integrations, data, and cloud services, and mentor a team of AI/ML and software engineers.
Define scalable, reusable architecture patterns supporting multiple business capabilities, personas, and workflows.
Determine appropriate use of 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 .
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Deliver domain AI agents for business areas such as payroll, benefits, labor contracts, financial planning, and competitive positioning , including cross-domain questions, scenario modeling, and financial analysis that reduce reliance on spreadsheets and manual reporting.
Define secure multi-persona architectures with appropriate 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 using appropriate relational, graph, vector, and search technologies, including ontology design and federated knowledge graphs (e.g., Stardog) that ground agent responses in governed enterprise data.
Define non-functional requirements for scalability, availability, performance, resiliency, security, maintainability, recoverability, and cost efficiency .
Design cloud-native architectures on Google Cloud Platform (GCP) using services such as Vertex AI, GKE, Cloud Run, AlloyDB, BigQuery, Pub/Sub, and Eventarc.
Establish patterns for AI observability, distributed tracing, LLM evaluation, accuracy benchmarking, monitoring, Responsible AI, and operational readiness , including test-question sets, UAT support, defect triage, and production release gates.
Define reference architectures, engineering standards, architecture decision records (ADRs), and reusable patterns, and lead engineering teams through hands-on implementation and code reviews.
Evaluate architecture alternatives through prototypes and proof-of-concepts and clearly communicate technical trade-offs to engineering, product, security, platform, and business stakeholders.
Guide CI/CD, Infrastructure as Code, containerization, DevSecOps, and production deployment patterns .

Required Qualifications

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 , with 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 with the ability to translate complex business requirements into scalable technical solutions.

Preferred Qualifications

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.

Technical Skills

Architecture: Distributed systems, microservices, REST APIs, event-driven architecture, stateful workflows, integration patterns
AI & Agentic: Generative AI, LLMs, LangGraph, LangChain, RAG, MCP, tool calling, semantic routing, multi-agent and human-in-the-loop patterns
AI/ML Engineering: Python, prompt engineering, embeddings, model selection and tuning, LLM evaluation and accuracy benchmarking
Cloud: GCP, Vertex AI, GKE, Cloud Run, BigQuery, AlloyDB, Pub/Sub, Eventarc
Data & Knowledge: PostgreSQL, BigQuery, Databricks, relational data modeling, vector search, semantic retrieval, ontologies, knowledge graphs (Stardog)
Security: IAM, RBAC/ABAC, OAuth/OIDC, service identity, secrets management
Observability: LangFuse, OpenTelemetry, Grafana, distributed tracing, AI/LLM evaluation and monitoring
Engineering: Git, CI/CD, Terraform, DevSecOps, containerization, automated testing

Additional Information

Job Grade: 304

Pay Range:

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $128,172/year to 272,736/year. Pay is based on several factors including but not limited to, market location and may vary depending on job-related knowledge, skills, and education/training and a candidate’s work experience. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance. Hired applicants may be eligible for 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, and Discounted Employee Stock Purchase Program.

Employee Type:

Permanent

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

Other Criteria:

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.

Basic Qualifications:

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.

About employer

More about this job

Skills

ML
Business intelligence
Payroll
Financial planning
LangChain
Financial analysis
Reporting
API development
Google Cloud
BigQuery
CI/CD
Infrastructure as code
Computer science
Microservices
Python
Workflow
Databricks
Design prototypes
Communication skills
Stakeholder management
Artificial intelligence
Data engineering
Grafana
Neo4j
SPARQL
PostgreSQL
Data modelling
Identity and access management
Git
IBM Terraform
Test automation

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