Interested in this AI/ML Engineer role at VF Corporation?
Apply Now →Skills & Technologies
About This Role
VF CORPORATION
Principal Agentic AI Engineer
ABOUT VF CORPORATION
Founded in 1899 and headquartered in Denver, Colorado, VF Corporation is one of the world’s largest apparel, footwear, and accessories companies. VF’s portfolio of iconic outdoor, active, and lifestyle brands includes The North Face, Vans, Timberland, Altra, Smartwool, Icebreaker, Kipling, Napapijri, Eastpak, and JanSport. VF markets its products across the Americas, EMEA, and Asia\-Pacific through wholesale partnerships, branded retail stores, and a growing direct\-to\-consumer ecommerce ecosystem.
Agentic AI represents a critical acceleration capability \- enabling VF to automate complex workflows, augment human decision\-making, and create intelligent systems across every function and every touchpoint.
POSITION SUMMARY
The Principal Agentic AI Engineer is a hands\-on technical leader embedded in VF Corporation’s centralized Agentic AI team. This team operates as a cross\-brand, cross\-region Center of Excellence that builds and deploys AI agents for every brand in VF’s portfolio and every function in VF’s enterprise.
This role carries a dual mandate that is critical to VF’s agentic AI strategy:
1\. Own the pro\-code development of production\-grade agentic AI, building custom agents, multi\-agent orchestration, complex integrations, and bespoke solutions that require deep engineering expertise.
2\. Evangelize and enable low\-code development across brand and business functions, empowering citizen developers to build, extend, and maintain their own agents while establishing the guardrails, patterns, templates, and governance that make decentralized agent creation safe and effective.
You are the engineer who builds the hardest agents yourself and makes it possible for everyone else at VF to build the rest. You set the architectural patterns, create the reusable components, establish the integration connectors, and define the quality standards that the entire VF organization inherits.
- Platform Polyglot: You think in terms of agent platforms and frameworks as a portfolio \- the right tool for each job. Copilot Studio for enterprise productivity \& Azure AI Foundry for custom builds.
- Governance by Design: governance is what makes innovation scalable. Build guardrails into every template, every connector, and every training so that decentralized agent creation is safe.
CORE PLATFORM RESPONSIBILITIES
Microsoft Copilot Studio — Low\-Code Enablement \& Pro\-Code Extension
- Own the pro\-code extension layer for Copilot Studio building custom connectors developing Power Platform dataflows and custom actions; and creating reusable component libraries.
- Implement advanced Copilot Studio capabilities multi\-agent orchestration, Model Context Protocol (MCP) server integration, human\-in\-the\-loop (HITL) approval workflows and Copilot Tuning.
- Build and enforce governance standards: lifecycle management, naming conventions, environment management, DLP policies, Entra Agent ID configuration, telemetry and analytics, and cost tracking.
- Develop the integration between Copilot Studio and Azure AI Foundry, creating seamless upgrade paths where agents that outgrow Copilot Studio’s capabilities.
Azure AI Foundry — Custom Agentic Application Development
- Architect and build production\-grade custom agentic applications on Azure AI Foundry using Azure OpenAI Service, Azure AI Search, Azure AI Agent Service, Prompt Flow, and Semantic Kernel.
- Build and maintain VF’s custom RAG pipelines and vector databases and develop agent evaluation and testing infrastructure on Azure: building task\-specific benchmarks, LLM\-as\-judge evaluation pipelines, red\-team testing harnesses, and regression test suites.
- Implement agent observability, tracing, monitoring, Application Insights, and custom telemetry \-providing VF’s Agentic AI team with real\-time visibility into performance, cost, error rates, and impact.
- Contribute to VF’s responsible AI practices by implementing guardrails, content safety filters, PII detection and masking, prompt injection defenses, and bias mitigation across all Azure AI Foundry.
LOW\-CODE EVANGELISM \& CITIZEN DEVELOPER ENABLEMENT
- Design and deliver training for Copilot Studio, structured in tiers: Foundational (business analysts building first agents), Intermediate (power users adding custom connectors and knowledge sources), and Advanced (technical builders using MCP, HITL, and multi\-agent patterns).
- Build and publish VF’s “Agent Developer Guide” and an agent template gallery \- a curated library of pre\-built, governance\-approved agent design patterns, connector libraries, governance requirements, testing standards, and deployment checklists.
- Track and govern developer adoption metrics: count, usage, business value delivered by decentralized agents, and quality/governance compliance rates—demonstrating the ROI.
- Identify when citizen\-built agents are hitting the ceiling of low\-code capabilities and transition them to pro\-code solutions, either by extending with custom connectors or migrating to Azure AI Foundry.
TECHNICAL RESPONSIBILITIES
Architecture \& Design
- Design the integration layer between VF’s agent platforms and enterprise systems of record using custom connectors, APIs, MCP servers, and event\-driven architectures.
- Architect secure, performant RAG pipelines that ground VF’s agents in enterprise knowledge with proper chunking, embedding, indexing, and retrieval strategies.
- Establish VF’s standards for agent memory, state management, and context handling across both short\-lived conversational interactions and long\-running autonomous workflows.
Development \& Delivery
- Write production\-quality code (TypeScript, Python, C\#) for custom agents, orchestration logic, tool integrations, evaluation pipelines, and deployment automation on Azure AI Foundry.
- Build and maintain CI/CD pipelines for agent development across both Copilot Studio and AI Foundry and develop custom connectors and MCP servers that expose VF’s enterprise systems to agents.
- Implement comprehensive agent testing: unit tests for tool functions, integration tests for system connectors, evaluation tests using LLM\-as\-judge and human review, and performance testing.
Governance \& Quality
- Define and enforce VF’s agent quality standards across both pro\-code and citizen\-built agents: accuracy, hallucination tolerance, response SLAs, cost, and brand voice.
- Implement governance automation for Copilot Studio: DLP policies, Entra Agent ID enforcement, environment controls, agent publishing approval workflows, and automated compliance scanning.
- Build VF’s agent observability stack: dashboards showing agent health, usage, cost, quality scores, escalation rates, and business impact across all brands and functions
- Own incident response for production agent issues: rapid diagnosis, remediation, root cause analysis, and post\-incident improvements.
QUALIFICATIONS
Required
- 8\+ years of software engineering experience (Senior) or 12\+ years (Principal), with at least 2 years focused on AI/ML systems, conversational AI, or agentic AI platforms.
- Deep hands\-on expertise with Copilot Studio, including both low\-code authoring (topics, knowledge, actions, agent flows) and pro\-code extension (custom connectors, Power Platform components, ALM).
- Strong production experience with Azure AI Foundry (Azure OpenAI Service, Azure AI Search, Prompt Flow, Semantic Kernel, Azure AI Agent Service) building custom agentic applications.
- Proficiency in at least two additional agent frameworks beyond the Microsoft ecosystem: LangChain/LangGraph, AutoGen, CrewAI, or comparable orchestration frameworks.
- Deep understanding of agentic AI patterns: multi\-agent orchestration, tool\-use and function calling, RAG architectures, embedding strategies, evaluation methodologies guardrails, and MCP.
- Strong software engineering fundamentals: TypeScript, Python, or C\#; RESTful and GraphQL API design; event\-driven architectures; CI/CD; infrastructure\-as\-code (Bicep, Terraform); containerization.
- Excellent communication skills with the ability to present to both engineering peers and business leaders, translating technical concepts into business\-relevant language.
- Bachelor’s degree in computer science, AI/ML, Engineering, or a related field. Masters preferred.
- Experience with Sierra AI or comparable AI platforms for consumer\-facing customer service agents.
- Background in Power Platform administration, governance, and toolkit deployment.
\#LI\-JB2
Hiring Range:
$144,000\.00 USD \- $180,000\.00 USD annuallyIncentive Potential: This position is eligible for additional compensation awards that may include an annual incentive plan, sales incentive, or commission potential. Specific details of the additional compensation eligibility for this position will be provided during the recruiting and interview process.
Benefits at VF Corporation: You can review a general overview of each benefit program offered, including this year's medical plan rates on www.MyVFbenefits.com and by clicking Looking to Join VF? Detailed information on your benefits will be provided during the hiring process.
*P**lease note, our hiring ranges are determined and built from market pay data. In determining the specific compensation for this position, we comply with all local, state, and federal laws.*
*At VF, we value a diverse, inclusive workforce and we provide equal employment opportunity for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws. If you require accommodations during the application process, please contact us at* *peopleservices@vfc.com**. VF will provide reasonable accommodations for qualified individuals to the extent required by applicable law.*
*Pursuant to all applicable local Fair Chance Ordinance requirements, including but not limited to the San Francisco Fair Chance Ordinance, VF will consider for employment qualified applicants with arrest and conviction records.*
Salary Context
This $144K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At VF Corporation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $144K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
VF Corporation AI Hiring
VF Corporation has 11 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ontario, CA, US, Denver, CO, US, Greensboro, NC, US. Compensation range: $45K - $180K.
Location Context
AI roles in Denver pay a median of $198,000 across 169 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.