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About This Role
Established in 2021, Independence Pet Holdings is a corporate holding company that manages a diverse and broad portfolio of modern pet health brands and services, including insurance, pet education, lost recovery services, and more throughout North America.
We believe pet insurance is more than a financial product and build solutions to simplify the pet parenting journey and help improve the well\-being of pets. As a leading authority in the pet category, we operate with a full stack of resources, capital, and services to support pet parents. Our multi\-brand and omni\-channel approach include our own insurance carrier, insurance brands and partner brands.
Role Summary
We are seeking a forward\-thinking Enterprise Principal Engineer to drive the development and deployment of autonomous AI agents on the Microsoft Copilot Studio (M365\) platform, with a strong emphasis on Power Apps, Power Automate, Dynamics 365, and Azure AI services.
This role is responsible for the hands\-on engineering, enhancement, and operational health of AI and agentic solutions across IPH platforms. The AI Engineering Lead will drive innovation in intelligent automation — enhancing customer engagement and operational efficiency in the pet insurance domain. This role will develop, integrate, and deliver agentic solutions that span enterprise platforms including Dynamics 365 CRM, Dynamics 365 Copilot agents, insurance applications, conversational interfaces, web, mobile, and back\-office systems of record.
The successful candidate operates comfortably at hands\-on levels — owning reference implementations, leading a growing engineering team, mentoring engineers, and partnering with product, data, security, and enterprise architecture leaders, operating within architecture guardrails set by AI Architecture, to deliver AI\-native customer experiences at scale.
Key Responsibilities
Copilot Studio (M365\)
- Build, configure, and deploy agentic AI solutions using Microsoft Copilot Studio, leveraging its low\-code/no\-code environment to rapidly prototype and scale autonomous agents for business processes.
- Design conversational and workflow agents that integrate with Microsoft 365 apps (Outlook, Teams, SharePoint) and enterprise data sources.
- Develop custom Copilot plugins and extensions to enhance agent capabilities, including natural language understanding, task automation, and contextual reasoning.
- Implement agent safety and transparency features per established governance frameworks and compliance standards.
- Partner with product owners and business stakeholders to translate business requirements into agent configurations and workflows.
- Agent action design — invoking Power Automate, Dataverse, REST APIs, and MCP servers.
- Produce sequence diagrams, decision trees, and agent contracts aligned to approved architecture patterns for consistent engineering execution.
Power Platform
- Develop and implement solutions using Power Apps for custom forms, dashboards, and mobile experiences that interact with agentic workflows.
- Build and maintain Power Automate flows to orchestrate multi\-step business processes, automate repetitive tasks, and integrate with external systems (ERP, CRM, insurance platforms).
- Leverage Power Platform connectors to enable seamless data exchange between Copilot agents, Dynamics 365 CRM, and other SaaS applications.
- Develop Power Apps components for agentic interfaces, including adaptive cards, PCF controls, embedded AI models, and workflow triggers.
Dynamics 365 Agents
- Build and configure Dynamics 365 Copilot agents for customer service, sales, and claims workflows, leveraging D365 native agent capabilities alongside Copilot Studio extensions
- Extend Dynamics 365 agent functionality through custom plugins, Dataverse integrations, and workflow automation to support insurance\-specific business processes.
- Collaborate with CRM and platform teams to ensure seamless integration between D365 agents and broader enterprise agentic solutions.
Operations, Delivery \& Lifecycle Management
- Own the delivery lifecycle for agentic and AI\-powered solutions — including new feature development, enhancements, defect resolution, upgrades, and production support across Copilot Studio, Power Platform, and Dynamics 365\.
- Manage solution packaging, environment promotion (Dev Test Prod), and CI/CD pipelines via Azure DevOps.
- Lead unit testing, integration testing, and UAT support to ensure production\-quality releases.
- Participate in Agile ceremonies (sprint planning, standups, retrospectives) and collaborate with PMO for delivery cadence and release governance.
- Adhere to IPH enterprise architecture standards and AI governance policies; escalate design decisions and pattern deviations to AI Architecture for review and approval.
Collaboration \& Stakeholder Engagement
- Work closely with product, engineering, and data teams to translate agent goals, reasoning capabilities, and ethical boundaries into delivered solutions.
- Partner with business stakeholders (Finance, HR, Operations) to align Copilot agent capabilities with business objectives.
- Collaborate with AI Architecture to ensure solutions align with approved patterns, guardrails, and governance frameworks.
Monitoring, Optimization \& Compliance
- Monitor agent performance and optimize using feedback loops, telemetry, and Power BI dashboards.
- Ensure delivered solutions meet SOC 2, PCI DSS, GDPR, and CCPA compliance requirements, embedding audit trails per enterprise architecture standards.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
- 12\+ years overall experience in application engineering and delivery.
- Including 5\+ years at Lead / Senior Engineer level on enterprise application platforms.
- 2\+ years hands\-on building production GenAI / agentic solutions (chat, copilots, multi\-agent orchestration, RAG).
- Strong proficiency in Python, C\#, or JavaScript.
- Hands\-on experience with Microsoft Copilot Studio, Power Apps, Power Automate, and Power Platform connectors.
- Proficiency in Dataverse — data modeling, customization, and plugin development.
- Experience building and configuring Dynamics 365 Copilot agents and extending agent capabilities within the D365 ecosystem.
- Familiarity with Dynamics 365 CRM, EIS Suite, and other insurance core systems.
- Experience integrating SaaS applications in a complex enterprise environment (identity management, data integration, workflow automation).
- Working knowledge of Azure cloud services, Azure Integration Services, and API Management.
- Experience with solution lifecycle management (managed/unmanaged solutions, ALM tooling) and CI/CD via Azure DevOps.
- Familiarity with source control (Git) and DevOps best practices.
Preferred Qualifications
- Experience with Azure OpenAI, Semantic Kernel, or other agentic AI frameworks.
- Microsoft certification in Power Platform, Azure, or Dynamics 365\.
- Background in insurance workflows and regulatory compliance.
- Experience with PowerApps Component Framework (PCF) for custom UI components.
- Experience with Adaptive Cards and Bot Framework for Teams\-based agent interfaces.
- Familiarity with Power BI for reporting and analytics on agentic workflows.
- Strong understanding of AI ethics, prompt engineering, and model governance.
- Experience ensuring systems comply with relevant regulations (GDPR, CCPA, SOC 2, PCI DSS).
All of our jobs come with great benefits including healthcare, parental leave and opportunities for career advancements. Some offerings are dependent upon the location of where you work and can include the following:
- Comprehensive full medical, dental and vision Insurance
- Basic Life Insurance at no cost to the employee
- Company paid short\-term and long\-term disability
- 12 weeks of 100% paid Parental Leave
- Health Savings Account (HSA)
- Flexible Spending Accounts (FSA)
- Retirement savings plan
- Personal Paid Time Off
- Paid holidays and company\-wide Wellness Day off
- Paid time off to volunteer at nonprofit organizations
- Pet friendly office environment
- Commuter Benefits
- Group Pet Insurance
- On the job training and skills development
- Employee Assistance Program (EAP)
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Independence Pet Holdings, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Independence Pet Holdings AI Hiring
Independence Pet Holdings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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