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About This Role
Purpose:
The Senior AI Engineer will design, build, and operate the framework and platform capabilities that enable agentic solutions across Floor & Decor — and will build flagship agentic products on that platform to prove it out. The role covers the full delivery lifecycle: planning, designing, configuring, testing, implementing, documenting, and maintaining AI solutions deployed on Microsoft Azure and integrated with the company’s operational systems.
The mandate is durable capability, not a single product. Individual agentic solutions will come and go — some will graduate to other teams, some will be retired — but the substrate they are built on is what this team owns: the tool and integration layer, context and memory management, evaluation and observability, guardrails, and the paved road that lets the next agentic workflow ship in weeks rather than quarters.
This is a hands-on senior individual contributor role. This is not a research or data science position. We are looking for an engineer with first-principles command of backend systems who has moved into AI engineering — someone who treats LLMs and agents as components in a well-architected distributed system, and holds them to the same standards of reliability, observability, security, and cost control as any other production dependency. Technical leadership here is exercised through architecture, code quality, and influence rather than through direct reports.
Minimum Eligibility Requirements:
- Engineering Foundation
- 7–10 years of professional software engineering experience, with increasing scope and ownership.
- Deep proficiency in backend engineering in at least one of C#/.NET, Java, Node.js/TypeScript, or Python. We care about first-principles understanding of backend systems, not a specific language — the ability to pick up a new stack quickly matters more than which one you arrived with. Python is a plus, not a requirement.
- Service-based and microservice architectures, RESTful API design, and asynchronous service communication — including API versioning, contract design, error semantics, and backward compatibility.
- Cloud-based serverless microservices in production (Azure Functions, Container Apps, or equivalent).
- Event-driven architecture: queues, pub/sub, idempotency, retries, and dead-letter handling.
- Solid data fundamentals: relational and NoSQL data modeling, query performance, and transactional correctness.
- Demonstrated ownership of code quality — automated testing, code review, and CI/CD as normal practice, not overhead.
- AI Engineering
- Designed and deployed agentic solutions — shipped and operated in production, not prototypes or POCs. We are interested in depth of agent delivery experience; retrieval-augmented generation is one tool in that kit, not the definition of the job.
- Harness engineering — practical command of the scaffolding that surrounds a model and determines whether an agent actually works in production: context construction and management, tool and function-call interfaces, multi-step planning and control flow, memory and state, structured output, guardrails and validation, human-in-the-loop checkpoints, and graceful failure and fallback behavior.
- Azure OpenAI services — LLM APIs, Azure AI Search (vector/index), and associated Azure infrastructure. Azure AI Foundry or AWS Bedrock experience also applies.
- Agent and LLM orchestration frameworks (LangChain, LangGraph, or similar).
- MCP (Model Context Protocol) or other tool-calling/function-calling patterns for LLM-to-system integrations.
- AI-specific failure and risk modes — hallucination, prompt injection, data leakage, non-determinism, runaway tool loops — and concrete techniques for mitigating them.
- Sound judgment on when an LLM or agent is and is not the right tool, and how to bound and validate its output.
- AI-Augmented Development
- Daily working fluency with agentic coding tools (Claude Code, Cursor, GitHub Copilot, Devin, or equivalent). Claude Code is our standard.
- A credible, specific point of view on where these tools accelerate delivery, where they do not, and how to review and test model-generated code responsibly.
- Able to speak to measurable impact on their own or their team’s throughput and quality.
- Cloud, Security, and Delivery
- Cloud environments (Azure preferred) including compute, storage, networking, and IAM fundamentals.
- Cloud security fundamentals: identity and access management, secrets management, and network boundaries.
- Agile/scrum methodologies — sprint ceremonies, story estimation, backlog grooming.
- Angular or comparable modern front-end frameworks — working knowledge.
- Clear written and verbal communication with both technical and non-technical partners; comfortable with ambiguity and able to move from a vague business problem to a scoped, specified, shippable increment.
Education
Bachelor’s degree in Computer Science, Engineering, or a related field — or equivalent professional experience. Demonstrated capability is weighted above credentials.
Essential Job Functions:
Agentic Platform & Framework Engineering
Design, build, and evolve the shared framework that lets teams across the organization build, deploy, and operate agentic solutions — a paved road covering agent scaffolding, tool and integration interfaces, context and memory management, evaluation, guardrails, and observability.
Turn one-off agent implementations into reusable building blocks: service templates, harness components, MCP server patterns, evaluation harnesses, and deployment pipelines.
Design and maintain MCP integrations with enterprise systems — inventory, merchandising, and the systems that follow — so any agent built on the platform can be grounded in live operational data without rebuilding the plumbing.
Define and document the standards, contracts, and reference architectures that agentic workloads at Floor & Decor are built against.
Make and document architectural decisions for reliability, scalability, security, and observability of AI services deployed in Azure.
Agentic Solution Delivery
Design, build, and operate production agentic solutions end to end — from problem framing through deployment and ongoing operation — using them both to deliver business value and to harden the underlying platform.
Apply the right technique to the problem: tool calling, multi-step planning, retrieval, memory, human-in-the-loop review, or a deterministic service where an agent is the wrong answer.
Build and tune retrieval where retrieval is warranted — chunking strategies, vector indexing, retrieval ranking, and context engineering on Azure AI Search.
Contribute across the stack, including an Angular front end and a Python-based service and LLMOps layer — picking up front-end work to get a feature over the line rather than handing it off.
Support the transition of mature agentic products to partner teams: documentation, runbooks, and knowledge transfer that let a solution outlive its original builders.
Evaluation, Observability & Operations
Establish evaluation and regression testing as a first-class part of the platform — eval sets, LLM-as-judge scoring, task-level success metrics, and regression gates in CI — so changes to prompts, models, tools, or retrieval ship with evidence rather than intuition.
Own evaluation pipelines for retrieval-based components using Ragas, tracking faithfulness, answer relevance, and context precision across releases.
Instrument agentic systems for observability with Langfuse alongside Azure Monitor — tracing, latency, token and cost attribution, tool-call success rates, quality signals, and failure modes — and act on what the telemetry shows.
Manage AI cost and performance: token budgeting, caching, model routing and right-sizing, and latency optimization.
Own production services — on-call participation, incident response, and post-incident follow-through.
Troubleshoot and resolve complex issues in agent behavior, retrieval quality, hallucination, latency, and integration reliability.
Engineering Practice & Collaboration
Apply spec-driven development: turn ambiguous business asks into clear specifications and acceptance criteria before code, and keep specs and implementation in sync.
Set and model the standard for AI-augmented development on the team: effective use of agentic coding tools, plus the review discipline that has to come with it.
Conduct code reviews and mentor peers, fostering a culture of quality and continuous learning — through technical influence rather than direct reporting lines.
Partner with product managers and business stakeholders to identify workflows worth automating, translate them into technical requirements, and help prioritize the backlog.
Act as a technical consultant to other teams adopting the platform — helping them build well on it rather than around it.
Participate in agile ceremonies — sprint planning, retrospectives, and daily stand-ups — as a senior voice on the team.
Communicate technical trade-offs and architectural decisions clearly to both technical and non-technical audiences.
Partner with security, data, and platform teams on data governance, PII handling, prompt injection defense, and responsible use.
Innovation & Quality
Evaluate emerging agent capabilities, tooling, protocols, and Azure OpenAI / AI Foundry updates; recommend and prototype improvements to keep the platform current in a fast-moving field.
Establish and maintain engineering best practices including CI/CD pipelines, infrastructure as code, code quality standards, and security practices for AI workloads.
Continuously reduce the cost and time required to bring the next agentic solution to production.
Nice to Have
- Spec-driven development — specification-first workflows, and using specs to drive AI-assisted implementation.
- Building internal developer platforms, frameworks, or SDKs consumed by other engineering teams.
- Building or publishing MCP servers, not just consuming them.
- LLM and agent evaluation frameworks — Ragas, G-Eval, LLM-as-judge, or agent trajectory evaluation.
- AI observability tooling — Langfuse (our platform) or equivalent tracing and evaluation systems.
- Retrieval infrastructure beyond Azure AI Search — pgvector, Pinecone, Elastic, or hybrid search design.
- Multi-agent orchestration, agent-to-agent protocols, or durable/long-running workflow engines.
- Infrastructure as code — Bicep, Terraform, or ARM.
- Fine-tuning and model adaptation — LoRA/PEFT, distillation, or evaluating fine-tuning against prompting and retrieval alternatives.
- LLMOps tooling such as MLflow, Weights & Biases, or Azure ML.
- Retail systems familiarity — POS, OMS, inventory/merchandising platforms.
- Center of Excellence or innovation-team experience within a larger enterprise.
- Open source contributions, technical writing, or speaking in the AI engineering space.
Our Technology Stack
Layer - Technologies
Front End - Angular, TypeScript
Back End / LLMOps - Python, C#/.NET, Node.js/TypeScript, REST APIs, serverless microservices (Azure Functions, Container Apps)
AI / LLM - Azure OpenAI, Azure AI Foundry, Azure AI Search, agentic
architectures, RAG where warranted
Orchestration & Integration - LangChain / LangGraph, MCP
Evaluation - Ragas, eval sets, LLM-as-judge, CI regression gates
Observability - Langfuse, Azure Monitor, Application Insights
Cloud & DevOps - Microsoft Azure, CI/CD pipelines, IaC (Bicep/Terraform), Agile/Scrum
AI-Augmented Development - Claude Code
Integrations - Enterprise systems — inventory, merchandising, and beyond — via MCP
Work Environment
- This is a hybrid position based at our Atlanta, GA headquarters, with a standard office schedule Monday through Friday during core business hours.
- You will work in a collaborative, open-plan office environment within the IT department, with dedicated space for focused engineering work.
- The role involves regular in-person collaboration with product managers, business stakeholders, and your engineering team.
- Occasional visits to retail store locations may be required to gather associate feedback and observe how the product is used in context.
- Some extended hours may be needed around major releases or on-call rotations for production incidents.
- Standard physical requirements of a professional office environment apply — prolonged sitting, use of a computer workstation, and participation in in-person and video meetings.
Why Join Our AI Center of Excellence
- Build the foundation, not just the feature. You will design the framework that every agentic workflow at Floor & Decor is built on — the decisions you make will shape how this company builds with AI for years.
- Ship production agentic products used by thousands of associates across a national retail footprint, and see the impact of your work in real stores.
- Greenfield scope in a mature company: the platform is being defined now, with real budget, real users, and real operational systems to integrate with.
- Work in an AI-augmented engineering environment where agentic development tooling is the standard, not an experiment.
- Competitive compensation, comprehensive benefits, and a culture that values curiosity and continuous improvement.
- Opportunities to grow into broader AI architecture and engineering leadership as the Center of Excellence scales.
Working Conditions (travel & environment)
- Travel may be required including air and car travel
- The noise level in the work environment is typically quiet to moderate.
PHYSICAL/SENSORY REQUIREMENTS
Sedentary Work – Ability to exert 10 - 20 pounds of force occasionally, and/or negligible amount of force frequently to lift, carry, push, pull or otherwise move objects. Sedentary work involves sitting most of the time, but may involve walking or standing for brief periods of time.
Note: Floor & Decor considers all applicants for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, medical condition, pregnancy, marital or familial status, veteran status, or based on any other class protected by applicable federal, state, or local law. Floor & Decor also provides reasonable accommodations to qualified individuals with disabilities in accordance with the Americans with Disabilities Act and applicable state and local law.
This job description does not constitute an employment agreement between the employer and the employee and is subject to change by the employer at its sole discretion.
Benefits & Rewards
Bonus opportunities & career advancement opportunities at every level
Programs that help you reach your financial goals: 401k with company match, Employee Stock Purchase Plan, and Referral Bonus Program
Medical, Dental, Vision, Life, and other Insurance Plans (subject to eligibility criteria)
Work-life balance, including:
- Paid vacation and sick time for eligible associates
- Paid holidays plus a personal holiday
- Paid Volunteer Time Off that starts on Day 1
Equal Employment Opportunity
Floor & Decor provides equal employment opportunities to all associates and applicants without regard to age, race, color, religion or creed, national origin or ancestry, sex (including pregnancy), sexual orientation, gender, gender identity, disability, veteran status, genetic information, ethnicity, citizenship, or any other category protected by law.
This policy applies to all areas of employment, including recruitment, testing, screening, hiring, selection for training, upgrading, transfer, demotion, layoff, discipline, termination, compensation, benefits and all other privileges, terms and conditions of employment. This policy and the law prohibit employment discrimination against any associate or applicant on the basis of any legally protected status outlined above.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Floor & Decor, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Floor & Decor AI Hiring
Floor & Decor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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