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About This Role
Job Description:
Sr. AI Engineer
Preferred Locations:Salt Lake City, UT; Louisville, KY, or Amsterdam (All Hybrid)
About Packsize
Packsize is redefining the way businesses and their customers use and experience packaging around the world. We build the technology, design the right solutions, and automate the processes that propel the industry forward. To us, packaging is much more than a box—it’s delivering what’s right for our customers, their customers, our people, and the planet.
About the Role
We are seeking an experienced AI Engineer to partner with our internal Data \& Analytics and IT teams to design, build, and operationalize production\-grade AI agents within the Microsoft ecosystem.
This role will focus on delivering enterprise\-ready solutions using:
- Microsoft Copilot Studio (low\-code orchestration)
- Azure AI Foundry (custom agent development \& advanced processing)
The engagement will operate in a co\-building model, working alongside consultants and internal teams to deliver initial AI pilot agents while establishing a scalable, governed AI platform.
What You'll Do:
AI Agent Architecture \& Design:
Define reference architecture for agentic AI solutions across Copilot Studio and Azure AI Foundry
Establish design patterns for:
- Retrieval\-Augmented Generation (RAG)
- Multi\-agent orchestration
- Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem)
Guide use\-case prioritization and platform selection (Studio vs Foundry vs hybrid)
AI Agent Development \& Delivery:
Build and deploy production\-grade AI agents, including:
- Knowledge \& troubleshooting agents
- Operational / workflow automation agents
- Data and analytics\-driven agents
- Implement:
- Prompt engineering and evaluation strategies
- Agent workflows and orchestration logic
- API, connector, and system integrations
Platform Foundation \& Governance:
Establish enterprise AI guardrails, including:
- Security, RBAC, and identity integration (Entra ID)
- Data access boundaries and governance
- Audit logging and monitoring frameworks
- Define and implement:
- Agent lifecycle (draft pilot production retirement)
- CI/CD pipelines and deployment standards
Azure AI \& Microsoft Ecosystem Implementation:
Configure and deploy:
- Azure OpenAI / model endpoints
- Azure AI Search (vector \+ semantic retrieval)
- Application Insights / Log Analytics monitoring
- Build and support:
- Copilot Studio environments and orchestration layers
- Azure AI Foundry\-based custom agent services
Co\-Development \& Enablement:
Work directly with consultants and internal teams to:
- Co\-build pilot agents
- Facilitate adoption and value\-based outcomes
Partner with data engineering team on:
- Data product and semantic layer alignment
- AI orchestration
- Observability and feedback loops
Enable internal teams on:
- Agent design patterns
- Responsible AI practices
- Ongoing support and scaling
What You'll Bring:
Technical Expertise:
Strong experience with:
- Azure AI services (Foundry, Cognitive Services, AI Search)
- Microsoft Copilot Studio / Power Platform
- Cloud\-native architecture (Azure)
Experience building:
- Conversational AI / chatbot / agent solutions
- RAG pipelines and LLM\-based applications
- API integrations, MCP frameworks, and enterprise workflows
Architecture \& Engineering:
Proven ability to design:
- Scalable, secure AI platforms
- Hybrid architectures (low\-code \+ pro\-code)
Experience with:
- MLOps and CI/CD pipelines
- Monitoring and observability (App Insights, logging, tracing)
- Secure cloud networking and identity
- Validating and optimizing AI systems
- Evaluating and selecting AI platforms and tools (cloud\-native and third\-party)
- Defining design patterns, standards, and guardrails for AI solutions
- Balancing rapid experimentation with maintaining platform consistency and avoiding fragmentation
Governance \& Responsible AI:
Experience implementing:
- AI governance frameworks
- Data security, privacy and compliance controls
- Lifecycle management and deployment gating
Understanding of regulatory considerations (e.g., data privacy, AI compliance risks)
- Experience with auditing, monitoring, and incident response for AI systems in production
Experience:
- 5\+ years in cloud / data / AI engineering or architecture roles
- 5\+ years in software development or data engineering
- Hands\-on experience building LLM\-based applications or agentic AI solutions
Preferred Qualifications:
Microsoft certifications (Azure AI Engineer, Azure Architect, etc.)
Experience integrating AI agents with:
- Microsoft Teams / M365
- ERP / CRM systems (SAP, Salesforce)
Familiarity with Databricks or lakehouse architectures
Experience delivering multi\-phase AI programs (pilot scale)
Engagement Model:
- First 90 days: co\-build solutions with Microsoft Partner
- Establish AI foundation and standards
- Drive adoption and value realization
- Create support model and roadmap enterprise AI solutions
What Success Looks Like:
- Production\-ready AI agents deployed within Microsoft 365
- Scalable AI agent platform established (governed, monitored, secure)
- Internal teams enabled to independently build and operate agents
- Clear pipeline of business\-driven AI use cases
- Consistent use of defined AI platforms and patterns across teams
- Reduced duplication and fragmentation across AI solutions
- Clear intake, prioritization, and decision\-making for AI use cases
- Align with vendor best practice to build, take ownership, and continue building value for the organization!
What We Offer:
The base salary range for this role is $140k\-$180k, however, Packsize considers several factors when determining compensation when extending a job offer, including but not limited to, the role being offered, the associated responsibilities, the candidate's prior work experience education / training, and any special skills. At Packsize, we’re committed to supporting the health, well\-being, and financial security of our team members. We offer a comprehensive benefits package that includes medical, dental, and vision coverage; a 401(k) retirement plan; Paid Time Off; Health Savings and Flexible Spending Accounts (HSA/FSA); and life and disability insurance. Additional voluntary benefits include critical illness, hospital indemnity, accident, and legal/ID theft protection. We also provide access to an Employee Assistance Program (EAP) to support your overall well\-being.
If this role excites you but you don’t meet each requirement listed, we encourage you to apply anyway. At Packsize, we welcome applicants of all backgrounds and experiences and understand that the best candidates may come from the most unlikely of places.
Packsize considers several factors when determining compensation when extending a job offer, including but not limited to, the role being offered, the associated responsibilities, the candidate's prior work experience, education/training, and any special skills. At Packsize, we’re committed to supporting the health, well\-being, and financial security of our team members. We offer a comprehensive benefits package that includes medical, dental, and vision coverage; a 401(k) retirement plan; Paid Time Off; Health Savings and Flexible Spending Accounts (HSA/FSA); and life and disability insurance. Additional voluntary benefits include critical illness, hospital indemnity, accident, and legal/ID theft protection. We also provide access to an Employee Assistance Program (EAP) to support your overall well\-being.
Packsize is an Equal Opportunity employer and is committed to diversity in its workforce. In compliance with applicable federal and state laws, Packsize policy of equal employment opportunity prohibits discrimination on the basis of race or ethnicity, religion, color, national origin, sex, age, sexual orientation, gender identity/expression, veteran’s status, status as a qualified person with a disability, or genetic information. Individuals from historically underrepresented groups, such as minorities, women, qualified persons with disabilities, and protected veterans are strongly encouraged to apply. Reasonable accommodations in the application process will be provided to qualified individuals with disabilities.
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Packsize, 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. This role's midpoint ($160K) sits 12% below the category median. Disclosed range: $140K to $180K.
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.
Packsize AI Hiring
Packsize has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Salt Lake City, UT, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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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