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About This Role
We are looking for an early\-career Junior AI Engineer to help build, test, monitor, optimize, and document enterprise\-grade AI solutions using Microsoft Azure AI Services. Reporting to the AI Engineer, you will contribute to backend services, autonomous agents, retrieval systems, and client\-facing tools that support the organization’s next generation of intelligent applications.
This role emphasizes hands\-on engineering, structured evaluation, operational monitoring, performance and cost optimization, and reusable technical guidance. You will work closely with AI, software, architecture, and infrastructure teams while developing broader ownership of defined AI components and operational processes.
Set up and configure AI\-powered services using Azure OpenAI, Cognitive Services, Azure AI Search, Azure Machine Learning, and Azure Functions.
Develop and maintain automated test suites for AI systems, including prompt regression, retrieval\-quality, response\-quality, safety, and end\-to\-end integration tests.
Create reusable evaluation datasets, test cases, scorecards, and performance baselines for AI applications and components.
Set up and maintain monitoring for response quality, failures, latency, token usage, model cost, retrieval performance, and other operational metrics.
Run structured experiments across models, prompts, retrieval settings, and search parameters to maintain quality while reducing token usage, latency, and operating cost.
Configure and optimize Azure AI Search, including semantic ranker, hybrid retrieval, vector search, filters, scoring profiles, top\-k settings, chunking strategies, and reranking approaches.
Evaluate alternative language, embedding, and reranking models using documented benchmarks and recommend appropriate options for defined use cases.
Create backend components and lightweight client\-side tools that expose AI capabilities across business systems.
Research emerging AI engineering practices, tools, frameworks, and vendor guidance, and translate findings into actionable recommendations.
Partner with engineers and architects to document approved AI patterns, implementation guidance, troubleshooting procedures, and best practices in Confluence.
Maintain developer\-focused versions of AI guidance as Cursor rules, repository instructions, templates, and reusable examples, keeping them synchronized with enterprise documentation.
Collaborate with AI engineers, enterprise architects, developers, infrastructure teams, and business partners to deliver secure, governed, and responsible AI solutions.
Qualifications
Experience:
0\-2 years of relevant software development experience through professional work, internships, academic projects, or personal projects using Python, C\#, JavaScript, or Java.
Experience or demonstrated familiarity with one or more Azure AI services, such as Azure OpenAI, Cognitive Services, Azure AI Search, Azure Machine Learning, or Azure Functions.
Familiarity with software testing concepts, including unit, integration, regression, and automated testing.
Ability to use metrics and structured evaluations to compare AI model, prompt, or retrieval performance.
Familiarity with application monitoring, logging, telemetry, dashboards, and operational troubleshooting.
Ability to analyze tradeoffs among response quality, reliability, latency, token usage, and cost.
Strong research, technical writing, collaboration, problem\-solving, and communication skills.
Preferred:
Experience orchestrating RAG pipelines or combining structured and unstructured data in AI workflows.
Exposure to LLM evaluation frameworks, AI observability platforms, prompt testing tools, or custom evaluation pipelines.
Experience experimenting with multiple language models, embedding models, rerankers, prompts, or retrieval configurations.
Familiarity with Azure Data Lake, Blob Storage, document intelligence, or enterprise\-scale datasets.
Experience developing simple client\-side applications using React, Streamlit, or JavaScript.
Familiarity with MLOps practices and model lifecycle management.
Familiarity with Confluence, Cursor rules, repository\-level instructions, or similar engineering knowledge\-management practices.
Understanding of enterprise architecture, IT compliance, responsible AI, and production deployment patterns.
Required Education:
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
Preferred Education:
Relevant coursework, internships, certifications, or project experience in artificial intelligence, machine learning, data engineering, or software engineering.
Microsoft Azure certifications, such as Azure AI Engineer Associate (AI\-102\) or Azure Data Scientist Associate (DP\-100\).
The estimated salary for this position ranges from $65,000 to $90,000 yearly. Actual compensation will vary based on factors including but not limited to the candidate’s skills, experience, and qualifications. Geographic differentials may also apply depending on the position’s location. There is no application deadline for this role; recruitment will remain open until the position is filled.
UniFirst is an equal opportunity employer. We do not discriminate in hiring or employment against any individual on the basis of race, color, gender, national origin, ancestry, religion, physical or mental disability, age, veteran status, sexual orientation, gender identity or expression, marital status, pregnancy, citizenship, or any other factor protected by anti\-discrimination laws.
If you require an accommodation during any part of the application process due to a disability or medical condition, please contact us by email at [email protected] or through our EthicsFirst portal at UniFirst.ethicspoint.com. You may also call the EthicsFirst Hotline at (800\) 213\-8979 to let us know the nature of your request.
UniFirst Recruiters and/or representatives will not ask job seekers to provide personal financial information when submitting a job application. Please be vigilant as such requests for information may be fraudulent.
Salary Context
This $65K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At UniFirst, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($77K) sits 64% below the category median. Disclosed range: $65K to $90K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
UniFirst AI Hiring
UniFirst has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Wilmington, MA, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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