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About This Role
Flex is the diversified manufacturing partner of choice that helps market\-leading brands design, build and deliver innovative products that improve the world.
A career at Flex offers the opportunity to make a difference and invest in your growth in a respectful, inclusive, and collaborative environment. If you are excited about a role but don't meet every bullet point, we encourage you to apply and join us to create the extraordinary.
Job Summary
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Flex is seeking a Platform Architect to design and scale enterprise\-grade platforms for deploying AI and data\-driven applications across our global manufacturing and supply chain ecosystem. This role will focus on building secure, scalable, and developer\-friendly platforms that accelerate the adoption of AI solutions—leveraging Google Cloud as a primary environment while enabling interoperability across Azure and AWS—and aligning with enterprise governance, compliance, and operational excellence standards.
You will operate at the intersection of AI platform engineering, cloud architecture, and developer experience, enabling teams across engineering, data science, and business functions to rapidly build and deploy production\-grade AI solutions.
What a typical day looks like:
AI Platform Architecture \& Deployment
- Architect and evolve enterprise AI platforms supporting LLMs, AI agents, and advanced analytics workloads
- Design scalable deployment patterns for AI solutions (APIs, agents, pipelines) across cloud environments
Enable self\-service AI capabilities through reusable platform services and standardized deployment frameworks
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Cloud Architecture \& Multi\-Cloud Strategy
- Lead architecture and implementation of AI and data platforms primarily on Google Cloud (Vertex AI, BigQuery, Cloud Run, Composer, etc.)
- Define and implement multi\-cloud design patterns spanning Google Cloud, Azure, and AWS, ensuring portability and flexibility
- Establish consistent infrastructure, networking, and governance models across cloud providers
Developer Platform \& Experience
- Build and evolve internal developer platforms that simplify deployment of AI and data applications
- Develop CI/CD frameworks that support consistent deployment across multi\-cloud environments
- Improve observability, reliability, and developer productivity through platform tooling and automation
Data \& AI Integration
- Design architectures integrating AI systems with enterprise data sources (ERP, supply chain, manufacturing systems) across cloud environments
- Enable real\-time and batch data pipelines using cloud\-native and cross\-cloud data services
- Support scalable, governed data platforms leveraging modern architectures (lakehouse, medallion, etc.)
Security, Governance \& Compliance
- Implement secure\-by\-design architectures across Google Cloud, Azure, and AWS, including IAM, network controls, and data protection
- Establish guardrails for AI deployment, including access control, monitoring, and auditability
- Ensure compliance with global regulatory standards (GDPR, FDA, and other industry requirements)
Cross\-Functional Collaboration
- Partner with Data Science, R\&D, IT, and business stakeholders to translate use cases into scalable platform capabilities
- Support lighthouse AI initiatives and drive broader enterprise adoption
- Mentor engineers and promote best practices in platform engineering and cloud architecture
What we're looking to add to our team:
- 8\+ years of experience in platform engineering, cloud architecture, or related roles
- Strong experience designing and deploying solutions on Google Cloud Platform
- Experience building and scaling cloud\-native platforms (APIs, microservices, data pipelines)
- Hands\-on experience with infrastructure\-as\-code (Terraform or similar) and CI/CD practices
- Experience supporting production\-grade data and analytics platforms
- Proficiency in Python, Java, Go, or similar
Preferred:
- Experience working in multi\-cloud environments (GCP, Azure, AWS)
- Experience with AI/ML platforms (Vertex AI, SageMaker, Azure ML, or similar)
- Familiarity with modern data architectures (BigQuery, Snowflake, lakehouse, etc.)
- Experience building internal developer platforms or self\-service tooling
- Exposure to enterprise security frameworks (IAM, zero trust, network segmentation)
- Experience in manufacturing, supply chain, or industrial environments
SS26
EA42
CA47
AA01
SA63
What you'll receive for the great work you provide:
- Full range of medical, dental, and vision plans
- Life Insurance
- Short\-term and Long\-term Disability
- Matching 401(k) Contributions
- Vacation and Paid Sick Time
- Tuition Reimbursement
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Application Deadline:
Applications for this job position will be accepted for at least five days following the job posting start date below and continuing until the end date below or until the position is filled. This posting may close sooner due to application volume.
Job Posting Start Date 08\-05\-2026 Job Posting End Date 08\-09\-2026\&\#xa;\&\#xa;
The base pay range for this position is provided below. The final base rate offered will be determined using job\-related, non\-discriminatory criteria, including but not limited to experience, qualifications, geographic location, education, external market data, and internal equity.
$173,700\.00 USD \- $238,900\.00 USD Annual
Job Category
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IT
Is Sponsorship Available?
No
Flex is an Equal Opportunity Employer and employment selection decisions are based on merit, qualifications, and abilities. We do not discriminate based on: age, race, religion, color, sex, national origin, marital status, sexual orientation, gender identity, veteran status, disability, pregnancy status, or any other status protected by law. We're happy to provide reasonable accommodations to those with a disability for assistance in the application process. Please email [email protected] and we'll discuss your specific situation and next steps (NOTE: this email does not accept or consider resumes or applications. This is only for disability assistance. To be considered for a position at Flex, you must complete the application process first).
Salary Context
This $173K-$238K range is above the median 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 Flex, 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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $173K to $238K.
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.
Flex AI Hiring
Flex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $238K - $238K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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