Sr. AI Software Engineer

About Frontdoor, Inc.

Frontdoor is reimagining how homeowners maintain and repair their most valuable asset – their home. As the parent company of two leading brands, we bring over 50 years of experience in providing our members with comprehensive options to protect their homes from costly and unexpected breakdowns through our extensive network of pre-qualified professional contractors. American Home Shield, the category leader in home service plans with approximately two million members, gives homeowners budget protection and convenience, covering up to 23 essential home systems and appliances. Frontdoor is a cutting edge, one-stop app for home repair and maintenance. Enabled by our Streem technology, the app empowers homeowners by connecting them in real time through video chat with pre-qualified experts to diagnose and solve their problems. The Frontdoor app also offers homeowners a range of other benefits including DIY tips, discounts and more. For more information about American Home Shield and Frontdoor, please visit frontdoorhome.com.

Responsibilities

We are looking for a highly capable Senior AI Software Engineer to design, build, and deploy production-grade AI-powered systems. This role focuses on applied AI, including large language models (LLMs), agent-based systems, reasoning workflows, and intelligent automation rather than traditional model training.

The Senior AI Software Engineer will be responsible for architecting and implementing scalable, secure, and reliable AI solutions that integrate with real-world applications and data systems. The ideal candidate combines strong software engineering fundamentals with hands-on experience building AI-driven systems and understands how to move from experimentation to production while maintaining quality, safety, and performance.

Key Responsibilities:

• Design and develop AI-powered applications and services
• Build systems leveraging LLMs, AI agents, reasoning engines, and orchestration frameworks
• Write clean, efficient, and maintainable custom code to support AI use cases
• Integrate AI services with existing software systems, APIs, and data sources
• Design prompts, workflows, tools, and guardrails for reliable AI behavior
• Optimize AI systems for performance, scalability, cost, and reliability
• Collaborate closely with product, design, and engineering teams to deliver end‑to‑end solutions
• Debug and resolve complex issues across AI logic, application code, and infrastructure
• Rapidly adapt to new AI capabilities, platforms, and evolving requirements
• Define and implement evaluation strategies to measure AI system quality, accuracy, and reliability
• Design systems that are resilient to prompt injection, data leakage, and misuse
• Implement observability, logging, and feedback loops for continuous improvement of AI systems
• Architect data flows and retrieval strategies (e.g., RAG, context management) to support AI applications

Qualifications

Required Skills:

• Strong software engineering experience with proficiency in one or more languages such as: Python, JavaScript/TypeScript, Java, C#, Go, or similar
• Experience building AI-driven applications using modern AI platforms or APIs
• Ability to design and implement custom logic, tools, and integrations (not just out‑of‑the‑box solutions)
• Experience working with APIs, services, and distributed systems
• Solid understanding of system design, application architecture, and software best practices
• Strong problem-solving skills and ability to operate independently
• Demonstrated flexibility and comfort working in fast‑changing environments
• Ability to balance rapid experimentation with production-quality engineering
• Clear communicator who collaborates well across teams
• Experience designing end-to-end AI systems, not just integrating APIs
• Understanding of AI system risks, including prompt injection, hallucination, and data exposure
• Experience with evaluation frameworks, testing strategies, or quality measurement for AI outputs
• Familiarity with retrieval-based systems (RAG) and context management
• Experience working in regulated or enterprise environments with security, compliance, or governance requirements is a plus.

Preferred Skills:

• Experience with LLM platforms and tooling (e.g., agents, function/tool calling, orchestration frameworks)
• Familiarity with prompt engineering, evaluation, and AI safety patterns
• Experience building AI copilots, chat systems, or intelligent automation
• Knowledge of cloud platforms and deployment patterns
• Experience working in agile or startup-style environments

Physical Role Requirements:

Be seated at a computer intermittently for up to eight hours a day. Move up to 10 pounds occasionally, by lifting, carrying, pushing, pulling, or otherwise repositioning objects. Sitting for long periods of time while using office equipment such as computers and phones. Repetitive motions using hands and digits; such as movement of wrists, hands and fingers while picking, pinching and typing during your normal working environment.

Minimum Education, Licensure and Professional Certification requirements:

Bachelor’s degree required in Computer Science, Engineering, Information Systems, or related field

Minimum Experience required:

5+ years of experience required in AI, machine learning, or prompt engineering, with demonstrated success in senior or lead roles

Travel:

Up to 20%

Compensation and Benefits:

This role pays between $140k to $185k, and your actual base pay will depend on your skills, qualifications, responsibilities, experience, and location. At Frontdoor certain roles are eligible for additional rewards and incentives. Speak directly to your recruiter to learn more.

Our approach to benefits is holistic, and includes health, wellbeing and financial components including: insurance for medical/pharmacy, dental, vision, life, and disability, weight loss and smoking cessation programs, matching 401(k) and ability to participate in our employee stock purchase plan.

Key skills/competency

• Senior AI Software Engineer
• Applied AI
• Large Language Models (LLMs)
• Agent-based systems
• Reasoning workflows
• Intelligent automation
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• System design
• Python