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About This Role
Position Overview: FUJIFILM Electronic Materials, U.S.A., Inc., is a global leader in chemical solutions which enable the semiconductor industry and the digital universe. We have an exciting opportunity at our Mesa, AZ facility for an Azure AI Cloud Engineer! The Role
The Azure AI Cloud Engineer designs, implements, and manages cloud-based infrastructure and services within the Microsoft Azure ecosystem. Provides support in Azure architecture, automation, networking, security, and performance optimization. Implements strong cloud principles, Infrastructure-as-Code (IaC), DevOps methodologies, and enterprise-grade security standards. Company Overview:
FUJIFILM Electronic Materials, U.S.A. fuels innovation in the semiconductor industry—where chemistry meets technology. Our advanced materials and formulations enable manufacturers to build the microchips that power Artificial Intelligence, computers, electric vehicles, smartphones, and more.
We’re a global business-to-business supplier of chemical formulations and advanced materials used for manufacturing and packaging semiconductors. Our chemical formulations allow top manufacturers to produce microchips that both power and connect our digital world. Think you’ve got what it takes to build a more connected world? Then connect with us today and challenge the boundaries of what’s possible—for the world and your future.
We have six U.S. manufacturing and Research & Development facilities, located in: Mesa, Arizona; Castroville, California; Hollister, California; Carrollton, Texas; and North Kingstown, Rhode Island—each offering unique local experiences, from vibrant cultural scenes to historic charm.
Fujifilm is globally headquartered in Tokyo with over 70,000 employees across four key business segments of healthcare, electronics, business innovation, and imaging. We are guided and united by our Group Purpose of “giving our world more smiles.” Visit: https://www.fujifilm.com/us/en/about/region/careers
Job Description: Key responsibilities for this role include:* AI & ML Integration: Build retrieval-augmented generation (RAG) pipelines and integrate Large Language Models (LLMs) like Snowflake Cortex and Azure AI into enterprise data architectures.
- Pipeline Development: Design and optimize complex ETL/ELT pipelines for structured and semi-structured data using tools like Azure Data Factory and Snowpipe.
- Snowflake Platform Administration: Provision and manage Snowflake compute warehouses, ensuring platform security, RBAC (Role-Based Access Control), and cost governance within the Azure environment.
- Cross-Functional Collaboration: Partner with Data Scientists, MLOps, and business stakeholders to operationalize AI use cases, from proofs-of-concept (POCs) to production scale.
- Automation & CI/CD: Develop infrastructure-as-code (IaC) and automate workflows using CI/CD pipelines (e.g., Azure DevOps, Terraform).
- Leadership & Project Management: Lead team coordination between IT functions and manufacturing needs. Manage large projects from concept to full implementation. Manage team members to meet deadlines and milestones.
- Configuring servers and databases
- Preserving data integrity
REQUIREMENTS* High School diploma or GED and a minimum of 12 years of experience is required.
- Bachelor’s degree in computer science or mathematics and 8 plus years of experience or an AS degree and 10 plus years of experience is preferred.
- A minimum of 5 years of experience supporting a manufacturing environment.
- Experience in project management and implementation.
- Experience with people management/mentorship.
- Hands-on experience with building AI models in Snowflake.
- Devops merging and branching knowledge required.
- PowerBI or similar reporting tool development experience.
- Experience working with Business Intelligence and Data Warehousing.
- Proficiency in Microsoft Azure cloud services, and cloud data architectures.
- Strong hands-on coding skills in Python, SQL, and occasionally JavaScript or Scala.
- Experience with Azure Synapse, Azure Data Factory, Snowpark, and modern GenAI application stacks (like LangChain or Vector Databases).
- Familiarity with data modeling, performance tuning (query optimization and warehouse sizing), and data security frameworks.
- Excellent logical and problem-solving abilities for abstract, creative concepts.
- Microsoft training and certification will be considered a strong asset.
- Self-motivated and self-directed.
- Very high attention to detail.
ADDITIONAL REQUIREMENTS
We value a professional, adaptable, and collaborative work environment. The following competencies are essential for success in any role and reflect our commitment to effective teamwork, problem solving, and workplace communication.* Resilience - Ability to adapt to workplace challenges, maintain professionalism, and manage responsibilities effectively.
- Communication - Capacity to clearly and professionally exchange ideas, interact respectfully with colleagues and customers, and foster positive workplace relationships.
- Reasoning & Decision Making - Ability to analyze information, follow verbal and written instructions, and make logical, sound decisions.
- Comprehension - Capability to understand and complete tasks as assigned and solve problems effectively.
- Organizational Skills - Competence in managing multiple priorities, maintaining accuracy, and staying focused despite potential workplace distractions.
To all agencies: Please, no phone calls or emails to any employee of Fujifilm about this requisition. All resumes submitted by search firms/employment agencies to any employee at Fujifilm via-email, the internet or in any form and/or method will be deemed the sole property of Fujifilm, unless such search firms/employment agencies were engaged by Fujifilm for this requisition and a valid agreement with Fujifilm is in place. In the event a candidate who was submitted outside of the Fujifilm agency engagement process is hired, no fee or payment of any kind will be paid. EEO Information:
Fujifilm is committed to providing equal opportunities in hiring, promotion and advancement, compensation, benefits, and training regardless of nationality, age, gender, sexual orientation or gender identity, race, ethnicity, religion, political creed, ideology, national, or social origin, disability, veteran status, etc. ADA Information:
If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to our HR Department ([email protected]).
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At FujiFilm, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
FujiFilm AI Hiring
FujiFilm has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mesa, AZ, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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