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About This Role
Position Summary:
Vallen Distribution is building a governed, production\-grade AI capability on an Azure\-first, Databricks\-centered architecture. This is not an advisory role — it is a hands\-on builder position with broad ownership across the AI layer.
The AI Platform Engineer will own the Databricks AI/ML platform layer, drive enterprise AI governance, review and remediate shadow AI solutions, and directly deliver automation use cases with business teams. You will be the internal AI expert for the organization — the first call for departments exploring automation and the technical authority on what gets built, how, and where data flows.
This is a greenfield opportunity with real scope and visibility. You'll work directly with the SVP of Data \& Technology Innovation and have immediate impact on a program that is active and growing today.
Core Responsibilities
Lakehouse AI Layer (Databricks) — \~35%
- Own the AI/ML layer on Databricks: feature stores, MLflow experiment tracking and model registry, and RAG/prompt architectural standards
- Define and enforce prompt engineering standards and LLM integration patterns across internal tools
- Partner with Data Engineering to design data pipelines that feed AI/ML use cases from the Unity Catalog lakehouse
- Evaluate and implement agentic frameworks (Claude API, Databricks AI agents) for internal automation
Automation Delivery — \~25%
- Directly build and deliver 2–3 automation use cases per year with business teams (HR, Legal, customer service, operations)
- Own full delivery lifecycle: scoping, design, build, testing, and handoff to platform operations
- Produce well\-documented, governed solutions — not one\-off scripts
AI Governance \& Shadow Solution Review — \~20%
- Serve as the first\-filter reviewer for all AI tools and platforms proposed for use at Vallen
- Partner with Security and Infrastructure to assess data classification risk, vendor posture, and integration risk before production deployment
- Review user\-built solutions (Claude Desktop, Copilot, Cowork, and similar tools) and determine when a productivity workflow has crossed into enterprise scope — then lead the governed rebuild
- Maintain Vallen's enterprise AI acceptable use policy, data classification guardrails, and platform\-tier standards
Use Case Evangelism \& Business Partnering — \~20%
- Embed with departments to surface and prioritize automation opportunities
- Maintain a scored use case backlog; facilitate structured discovery sessions with business stakeholders
- Serve as the internal AI resource teams engage before going external
Requirements:
Job Qualifications:
- 2–4 years of hands\-on experience in AI/ML engineering, data engineering, or a closely related technical role
- Hands\-on experience with Databricks — MLflow, notebooks, Unity Catalog, and Python/SQL workflows; or equivalent lakehouse platform experience with demonstrated ability to ramp quickly
- Practical experience building with LLMs: prompt engineering, RAG pipelines, or API integration— production or project\-level experience counts
- Strong Python skills; ability to own full solution delivery from prototype to production
- Solid grounding in Azure: Azure OpenAI, Azure Data Lake Storage, Azure Key Vault, and core networking/security concepts
- Demonstrated ability to work across technical and non\-technical stakeholders — you can explain what you're building and why it matters
- Experience reviewing or documenting AI/ML solutions for risk, data sensitivity, or governance considerations
Preferred Qualifications:
- Experience in distribution, supply chain, or industrial B2B environments
- Familiarity with agentic frameworks: Databricks AI Agent Framework, LangChain, AutoGen
- Exposure to enterprise AI governance concepts: acceptable use policies, data classification tiers, model risk review
- Experience with Azure DevOps (ADO), CI/CD pipelines)
- Familiarity with Power BI, Databricks Genie, or other BI/AI consumption layers
- Working knowledge of MDM, ERP data structures, or multi\-system data environments
Work Environment: (Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.):
- Long periods of time working on a computer and performing repetitive key\-boarding activities.
Physical Demands: (Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.):
- While performing the duties of this job, the employee is regularly required to talk and hear. The employee frequently is required to sit. The employee is occasionally required to stand and walk. The employee may be required to occasionally lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, and ability to adjust focus.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vallen, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Vallen AI Hiring
Vallen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Belmont, NC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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