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About This Role
Overview
AI Engineer
*This position is hybrid working from our Legacy West Support Center located in Plano, Texas*
About Sally Beauty Holdings, Inc.
At SBH, our purpose is to inspire a more colorful, confident, and welcoming world. We are the leader in professional hair color, selling and distributing professional beauty supplies across 11 countries through our Sally Beauty and Beauty Systems Group businesses. Sally Beauty offers products for hair color, hair care, nails, and skin care to retail customers looking for salon quality products at a value price. Beauty Systems Group, branded as Cosmo Prof or Armstrong McCall stores, along with its direct sales consultants, sell professionally branded products intended for use and resale by salons to retail consumers.
About the role
You will work within our enterprise AI governance framework — collaborating with business stakeholders, data engineers, and platform teams — to deliver scalable, secure, and production\-ready AI applications. From LLM\-powered workflows to agentic automation pipelines, you will play a pivotal role in shaping how Sally Beauty leverages generative AI.
Responsibilities
- Design, develop, and deploy AI/ML models and generative AI solutions into production environments using Azure AI Foundry, Azure OpenAI, and AKS\-based microservice architectures.
- Build and maintain LLM\-powered applications including prompt engineering frameworks, RAG pipelines, agent orchestration, and MCP (Model Context Protocol) server integrations.
- Collaborate with stakeholders to translate business requirements into AI proof\-of\-concept (POC) builds and production\-ready features, including tooling for marketing, supply chain, and retail operations.
- Develop and maintain CI/CD pipelines for AI workloads using GitHub Actions and ArgoCD, adhering to enterprise AI governance, security, and SDLC standards.
- Implement responsible AI practices including model evaluation, bias detection, hallucination mitigation, observability, and audit logging across all AI services.
- Partner with security and infrastructure teams to ensure AI systems comply with Zero Trust principles, data privacy requirements, and enterprise identity standards via Entra ID and Azure Key Vault.
- Contribute to internal AI sandbox environments, enabling both professional and citizen developers to experiment with generative AI tools safely and within governance guardrails.
- Document AI solution architectures, API contracts, and integration patterns; present findings and recommendations to cross\-functional teams and leadership.
Knowledge, skills \& abilities requirements
- 3\+ years in software engineering or data science, with at least 1 year in AI/ML engineering or applied generative AI development.
- Bachelor’s degree in computer science, Data Science, Engineering, or a related technical field required.
- Proficiency in designing and deploying LLM\-powered solutions including fine\-tuning, prompt engineering, retrieval\-augmented generation (RAG), and agentic frameworks such as LangChain, AutoGen, or CrewAI.
- Hands\-on experience with Azure AI Foundry, Azure OpenAI Service, and AKS\-based deployments for AI workloads. Familiarity with Azure Entra ID, Key Vault, and Managed Identity for secure AI service integration.
- Strong programming skills in Python including FastAPI, Pydantic, and asyncio. Experience with REST API design, JSON schema modeling, SQL, and Infrastructure as Code tools such as Terraform or Bicep.
- Working knowledge of vector databases such as Qdrant, pgvector, or Weaviate and their role in semantic search and RAG architectures.
- Experience with model evaluation, A/B testing, and drift detection to maintain production model quality over time.
- Solid understanding of CI/CD practices using GitHub Actions and ArgoCD for deploying AI services in containerized environments.
- Strong written and verbal communication skills with the ability to translate complex technical concepts for non\-technical stakeholders. Self\-directed, ownership\-oriented, and effective in cross\-functional team settings.
Preferred Qualifications
- Hands\-on experience building production LLM applications with structured outputs, tool/function calling, and multi\-agent orchestration patterns.
- Familiarity with Model Context Protocol (MCP) server design patterns and building context\-aware AI tool integrations.
- Prior experience in retail, CPG, or large\-scale enterprise environments where AI outcomes directly impact store operations or customer experience.
- Understanding of responsible AI frameworks, AI risk management, and enterprise AI SDLC governance practices including audit trails and policy enforcement.
- Experience with AI\-assisted developer tooling such as Claude Code, GitHub Copilot, Cursor, or Gemini CLI for accelerated development workflows.
Competencies \& attributes
- Passionate Learner – inquisitive about the business; open to feedback and coaching, applies learning quickly; applies learning to improve processes and procedures, proactively shares learning with colleagues and leaders; realigning and reshaping projects
- Flexible \& Agile Adapter – responsive and open to change; works well with ambiguity; adapts to new plans or directions; keeps calm under pressure; perseveres to achieve the plan/task; doesn’t dwell on the past
- Talent Builder – considers how we can create an inclusive culture; encourages input from others; invests time as an informal/formal coach or buddy; works to build a diverse team with the right skills and knowledge; looks for ways to acknowledge, motivate, and value the team
- Effective Communicator – articulates in an appropriate and accurate manner; emotionally astute while remaining authentic to own style/self; encourages others to express views and opinions; demonstrates active listening and uses probing questions; is concise and relevant with data/info
- Team Builder – references the importance of teamwork and actively demonstrates collaboration and sharing; builds and/or participates in effective teams; values the importance of inclusion and various sources of thought/input; humble when operating within a team
- Customer Focused Partner – understands internal and external customer needs; contributes to plans and actions to improve the associate and customer journey/experience; holds self and team accountable for improving the customer experience; is an advocate for the customer
- Strategic Thinker – progressive thinking with the ability to bring new ideas to life; works with others to develop progressive and cost\-effective strategies; provides suggestions to improve upon continuous improvement and scalability within department; uses a broad range of data sources
- Big Picture Thinker – understands own department and how other key departments operate; adopts an inclusive approach; seeks feedback reviews progress, and adapts plans as needed; understands interdependencies with other departments
- Results Driver – effective at driving and delivering on plans; holds self and team accountable to high standard of delivery; suggests opportunities for innovation and continuous improvement; focuses on the right priorities and uses resources/time wisely; demonstrates grit and determination
- Problem Solver \& Decision Maker – able to consume department/operational data to identify business; identifies, gathers, and examines the relevant information; makes recommendations and takes action to solve challenges, considers importance/impact of decisions against relevant factors
Working conditions \& physical requirements
This will be a hybrid role required to be onsite at the Corporate office on specified days. The work environment generally involves everyday risks or discomforts which require normal safety precautions typical of such places as offices, meeting and training rooms, retail stores, and residences or commercial vehicles, e.g., use of safe work practices with office equipment, avoidance of trips and falls, observance of fire regulations and traffic signals, etc. The work area is adequately lighted, heated, and ventilated.
The work is sedentary; however, occasional travel to company locations may be required. Typically, the employee may sit comfortably to do the work. However, there may be some walking; standing; bending; carrying of light items such as papers, files, books, small parts; using a keyboard, driving an automobile, etc. No special physical demands are required to perform the work.
\#LI\-Hybrid
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Sally Beauty, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Sally Beauty AI Hiring
Sally Beauty has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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