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About This Role
- Great Work Environment
- Exploring and Leading
- Supportive Team
Position Summary
The Data and AI Platform Developer serves as Flexfab's hands\-on data steward and guardian of data quality, security, and compliance for API, AI integration, and analytics initiatives. This role ensures AI and company\-data usage are based on accurate, trusted, properly classified data, while preventing sensitive information from being exposed externally or entering the AI ecosystem without approval. The role also owns ERP data mapping for BIRST reporting and dashboards and administers data across MS\-SQL, data lakes, Dayforce data, and other enterprise sources.
Essential Duties and Responsibilities
- Own enterprise data quality by establishing trusted data structures and ensuring AI agents and analytics solutions consume accurate, complete, and timely data
- Apply and enforce data security rules, including customer\-contract restrictions, payment card exclusions, and personal\-data protections
- Act as the compliance gatekeeper by reviewing, approving, and documenting sensitive data flowing into AI systems
- Perform ERP data mapping for BIRST reporting and dashboards
- Administer and develop data from MS\-SQL Server, Infor Data Lake, Dayforce, and other business database sources
- Support custom applications, AI integration, data analytics, and overall application integration with curated, governed datasets
- Partner with Flexfab IT, ERP, Infor/Velocity, and Copilot Studio resources to certify datasets that feed AI agents and automations
- Maintain data dictionaries, lineage documentation, classification taxonomies, and access\-control matrices
- Define and operate data\-quality monitoring, profiling, and remediation processes
- Coordinate with the QMS team and IT Helpdesk on change\-control records for data\-related changes
- Consolidate and administer company legacy data warehouse information
Success Measures
- Data quality: AI agents and analytics solutions consume accurate, complete, timely, and trusted data
- Compliance protection: sensitive data is reviewed, approved, documented, and protected before entering AI systems
- Integration readiness: ERP, MS\-SQL, data lake, Dayforce, and other enterprise datasets are mapped, governed, and usable
- Governance adoption: data dictionaries, lineage, classifications, access controls, and change records are maintained
- Business impact: trusted AI and analytics are enabled while reducing compliance and data\-exposure risk
Core Competencies
- Decision Quality \- makes timely, sound decisions that protect data integrity, compliance, and business outcomes
- Plans and Aligns \- organizes governance work, data mapping, quality monitoring, and remediation around business priorities
- Action Oriented \- addresses data\-quality, security, and integration needs with urgency and follow\-through
- Drives Results \- delivers trusted datasets, reliable reporting support, and practical governance improvements
- Collaborates \- works effectively with IT, ERP, Infor/Velocity, Copilot Studio, QMS, Helpdesk, and business data owners
- Situational Adaptability \- adjusts approach across data sources, privacy requirements, analytics needs, and AI use cases
Required Qualification
Education
- Bachelor's degree in Information Systems, Data Science, Computer Science, Statistics, or related discipline; equivalent combination of education and relevant work experience will be considered
Experience
- Five to eight years in data management, data engineering, or data governance roles
- Minimum two years working with Microsoft SQL Server and a cloud data lake platform, such as Infor, Microsoft, Amazon, or comparable environments
- Demonstrated experience applying data classification and privacy frameworks, including examples such as TISAX, ITAR, CMMC, GDPR, PCI\-DSS, and personal information protections
- BIRST or comparable enterprise BI tool experience preferred, including tools such as Power BI
- Dayforce or comparable HCM data\-integration experience preferred
- Experience supporting AI/ML or analytics teams with governed, curated datasets
Skills and Tools
- Strong T\-SQL skills, including stored procedures, views, and performance tuning
- Power BI model authoring, row\-level security, and dataset governance experience
- Data classification, lineage, master data management, data dictionaries, taxonomies, and access\-control practices
- Infor OS, Data Fabric, BIRST training, Microsoft SQL training, project or team lead experience, or MDM program experience preferred
- Strong communication and stakeholder\-management skills, including the ability to say 'not yet' when needed to protect compliance
- Detail\-oriented, audit\-minded, policy\-driven, highly collaborative, and comfortable working independently with minimal supervision
Cultural Alignment
Flexfab lives its Humble HEARTS values. The Data and AI Platform Developer demonstrates and promotes:
- Honesty – Lead with integrity and transparency in technical decision\-making and stakeholder interactions.
- Excellence – Relentlessly pursue innovative solutions that exceed expectations in product performance, safety, and quality.
- Accountability – Own the outcomes of technology programs and foster a culture of responsibility within the team.
- Respect – Champion collaboration and value the diverse expertise of engineers, scientists, and partners across functions.
- Teamwork – Build strong cross\-functional teams that work in unison to deliver results from concept through launch.
- Support – Enable professional growth and promote a balanced, high\-performing environment that benefits individuals, the organization, and broader community.
Working Conditions
- Office\-based at Flexfab headquarters in Hastings, MI
- Occasional travel to other Flexfab sites or vendor/partner locations, estimated under 10%
- Extended periods of computer work and participation in cross\-time\-zone meetings as needed to support global operations
- Occasional after\-hours support during system go\-lives, releases, or critical production issues
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 Flexfab, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Flexfab AI Hiring
Flexfab has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hastings, MI, US.
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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