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About This Role
Who We Are
At Academy Sports + Outdoors our vision is to be the best sports + outdoors retailer in the country — but what truly sets us apart is our people. We’re a passionate, purpose-driven team that’s as committed to each other as we are to our customers.
We’ve spent over 80 years building a culture that puts people first. We believe in creating opportunities for growth, fostering meaningful connections, and supporting every Team Member’s journey. What fuels us? Our belief in the power of fun.
Here, you won’t just help customers gear up for their next adventure — you’ll launch one of your own. Whether you're starting out or leveling up, Academy is a place where fun can’t lose!
What You Will Work On
In this role, you design, implement, operate, and continuously improve the enterprise platforms and integration capabilities that support AI agents and generative AI solutions. Your work helps teams move approved AI solutions from prototype to reliable production use across Microsoft Azure, Microsoft 365, Power Platform, Google, and other approved enterprise AI technologies. You provide technical leadership for platform services that are secure, scalable, observable, cost-managed, governed, and supportable across development, test, and production environments.
Working across the platform lifecycle, you establish standards for source control, release management, deployment pipelines, environment separation, production readiness, and operational support. You implement governance controls for inventories, ownership, risk tiering, identity, access, data protection, runtime protection, audit logging, and periodic recertification, while also managing model access, quotas, capacity, private networking, endpoints, and onboarding controls. Your work also includes building telemetry and consumption controls, enabling secure integrations with enterprise systems, embedding automated evaluation and compliance checks, documenting reusable patterns, leading technical initiatives, mentoring engineers, and evaluating emerging capabilities that can improve security, reliability, scalability, efficiency, and adoption.
Who You Will Work With
You will partner across AI engineering, Cybersecurity, Identity, Enterprise Architecture, Data, Legal, Finance, and business teams to ensure AI platforms and integrations are secure, governed, supportable, and cost effective. You will also work directly with engineers, platform users, solution owners, business power users, and approved citizen developers, providing standards, documentation, enablement materials, technical guidance, and mentorship that support safe and consistent adoption.
What You Bring
Bring your energy, your ideas, and your love for sports and the outdoors.
- Bachelor's degree in Computer Science, Information Technology, Engineering, Data, or a related field, or equivalent practical experience
- 7+ years of experience in platform engineering, infrastructure engineering, DevOps, MLOps, cloud engineering, integration engineering, or related disciplines
- 2+ years of experience building or operating platforms for large language model, generative AI, AI agent, or intelligent automation workloads
- Experience designing, implementing, and supporting enterprise-scale cloud platforms, shared services, or internal developer platforms used by multiple teams
- Hands-on experience with Microsoft Azure, Microsoft 365, Entra ID, Copilot Studio, Power Platform administration, Azure AI services, or comparable enterprise platforms
- Experience implementing infrastructure as code, source control, release gates, CI/CD pipelines, environment management, and production support practices
- Experience integrating enterprise applications through REST APIs, OAuth, service-to-service authentication, connectors, workflow tools, and systems of record
- Experience implementing platform monitoring, incident response, usage telemetry, cost controls, audit logging, and operational reporting
- Experience leading complex technical initiatives, establishing standards, and mentoring or guiding engineers and platform users
- Retail, regulated, or multi-site enterprise experience preferred
- Microsoft Azure, Microsoft Power Platform, identity and access management, cloud security, DevOps, MLOps, or related platform certification preferred
- Strong knowledge of cloud platform architecture, AI platform engineering, environment strategy, networking, identity, access controls, and enterprise security practices
- Deep understanding of authentication, authorization, least-privilege access, data protection, auditability, privacy, and responsible AI controls
- Ability to design and operate development, test, and production environments based on solution risk and support requirements
- Practical knowledge of Azure AI services, Copilot Studio, Power Platform, Microsoft 365, Microsoft Graph, Entra ID, Purview, Defender, or comparable technologies
- Experience with infrastructure as code and CI/CD technologies such as Bicep, Terraform, Azure DevOps, GitHub Actions, or comparable solutions
- Experience with observability, incident management, performance monitoring, usage analytics, cost telemetry, and consumption controls
- Ability to design reusable identity, logging, evaluation, connector, integration, and deployment patterns for AI-enabled solutions
- Proficiency in Python, PowerShell, or another scripting language used for platform automation
- Strong analytical, troubleshooting, documentation, and problem-solving skills in distributed enterprise environments
- Ability to influence cross-functional teams, explain technical controls to technical and non-technical stakeholders, and provide technical leadership and mentorship
- A team-first attitude; while your core duties are your priority, you're happy to step in wherever you're needed
Work Style and Physical Requirements
- Flexible work schedule may be required to support business needs, platform changes, production incidents, or scheduled maintenance
- Regular in-office attendance is required
- Acceptable level of hearing and vision to perform job duties
- Ability to adhere to company policies and professional standards
Key Skills
Deployments, Lifecycle Management, Logging, Integration Projects, Dashboard Metrics, Information Assets Protection, Security Access
Equal Employment Opportunity
Academy is an Equal Opportunity Employer and does not discriminate with regard to employment opportunities or practices on the basis of race, religion, national origin, sex, age, disability, gender identity, sexual orientation, or any other category protected by law.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Academy Sports + Outdoors, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Academy Sports + Outdoors AI Hiring
Academy Sports + Outdoors has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Katy, TX, US, Katy, TX.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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