{"schemaVersion":"jobsearcher.job.v1","id":"dc4f9b3f165d2e1215cc698c","url":"https://jobsearcher.com/jobs/dc4f9b3f165d2e1215cc698c","canonicalUrl":"https://jobsearcher.com/jobs/dc4f9b3f165d2e1215cc698c","title":"Full Stack Developer / AI Focused","description":"Job Description Job Description About Us\r\nWe are a fast-growing technology company building products used by hundreds of thousands of customers, backed by a supply chain spanning multiple distribution centers and a data platform processing millions of events daily. Engineering is not a support function here — it is a core driver of every major business outcome.\r\nWe move fast, hold ourselves to a high bar, and believe the best engineering decisions are made by people who are close to the business. We are embracing AI not as a trend, but as a genuine multiplier — using it to ship better software, faster, while never losing sight of the craftsmanship that makes software great.\r\nThe Role\r\nThis is a forward-deployed engineering role with AI at its core. You will architect systems, write production-grade code, design databases, and own your projects end to end — with a primary focus on deploying AI capabilities directly into the business and products that serve real users. What defines this role is the expectation that you bring strong engineering fundamentals together with a hands-on, deployment-first mindset for AI-powered tools and workflows.\r\nYou are not required to be an AI researcher or an ML specialist. You are expected to be an excellent engineer who deploys AI where it creates real value — using it to accelerate delivery, build intelligent features, and solve hard problems, while applying your own judgment to validate, refine, and own the outcome.\r\nStrong Engineering Core\r\nDesign robust, scalable systems from the ground up\r\nWrite clean, well-tested, maintainable code\r\nOptimize database performance and data models\r\nDebug complex issues across the full stack\r\nOwn code quality through rigorous peer review\r\nDeliver reliable software with measurable outcomes\r\nAI as a Force Multiplier\r\nUse AI coding assistants to accelerate development\r\nLeverage LLMs to generate boilerplate, tests, and docs\r\nBuild AI-powered features where they add real user value\r\nApply AI to improve code review, debugging, and analysis\r\nEvaluate AI outputs critically — judgment still wins\r\nStay current and bring new AI tools to the team\r\nOur Culture\r\nWhat We Believe\r\nOutcomes over activity — we measure what ships and what works.\r\nSpeed is a feature — long approval chains kill great products.\r\nRadical candor — honest feedback is a form of respect.\r\nLearning is non-negotiable — every sprint is a chance to improve.\r\nNo politics, no silos — collaborate openly across every team.\r\nHow We Work\r\nFast-paced sprints with a strong bias toward shipping.\r\nEngineers own requirements, architecture, and roadmap input.\r\nAI tools are standard kit — we share what works.\r\nBlameless post-mortems — failure is a learning event.\r\nAsync-first with intentional synchronous collaboration.\r\nKey Responsibilities\r\nCore Software Engineering (50%)\r\nDesign, build, and maintain scalable, high-quality software systems and APIs that serve real users in production.\r\nWrite clean, well-structured code with appropriate test coverage — unit, integration, and end-to-end.\r\nArchitect and optimize relational and non-relational database schemas, queries, and data models for performance and reliability.\r\nConduct meaningful code reviews that improve team quality and share knowledge, not just catch syntax errors.\r\nDebug, profile, and resolve performance bottlenecks and production issues with urgency and rigor.\r\nContribute to technical architecture decisions — propose solutions, evaluate tradeoffs, and document outcomes.\r\nParticipate actively in Agile ceremonies: sprint planning, standups, retrospectives, and backlog refinement.\r\nAI-Forward Deployment (35%)\r\nDeploy AI solutions end-to-end — from identifying the right use case, to building and shipping LLM-powered features directly into products and internal workflows.\r\nIncorporate LLM APIs and AI frameworks into product features where they create genuine user value: search, summarization, recommendations, intelligent automation, and decision support.\r\nApply critical engineering judgment to evaluate, refine, and validate all AI-generated outputs before they reach production — you own the result, not just the prompt.\r\nUse AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a daily accelerator — and champion effective AI tool patterns and prompt strategies across the team.\r\nStay at the front edge of the AI tooling landscape — evaluate new models, frameworks, and techniques and bring back deployment-ready recommendations that move the business forward.\r\nCross-Team Collaboration & Communication (15%)\r\nPartner with Marketing, Customer Experience, Data Science, Merchandising, Warehouse Ops, and Finance to understand requirements and deliver technical solutions.\r\nTranslate technical concepts clearly for non-technical stakeholders — written documentation, presentations, and live discussions.\r\nPresent project outcomes and architectural decisions to senior leadership with confidence and clarity.\r\nContribute to a culture of knowledge sharing: write internal documentation, run team demos, and mentor peers.\r\nCross-Team Collaboration\r\nEngineering here is a visible, active partner across the business — not a back-room function. You will work directly with teams who depend on the systems and data you build. Strong communication and commercial awareness are just as important as great code.\r\nMarketing: Personalization, campaign analytics, A/B platforms\r\nCustomer Experience: AI support tools, self-service flows, CSAT pipelines\r\nData Science: Model integration, feature engineering, shared infra\r\nMerchandising: Pricing, inventory intelligence, catalog tooling\r\nWarehouse Ops: Fulfillment automation, routing, operational dashboards\r\nFinance & Ops: Cost models, reporting pipelines, forecasting\r\nSupply Chain: Vendor integrations, PO systems, logistics optimization\r\nProduct: Feature scoping, roadmap input, rapid prototyping\r\nSecurity: Secure design, data privacy, compliance tooling\r\nRequired Qualifications\r\nEngineering Fundamentals — Non-Negotiable\r\nExperience: 3–6 years of professional software engineering in a production environment, with a portfolio of real systems you have owned and shipped.\r\nLanguages: Strong proficiency in one or more of: Python, Java, TypeScript, Go, or C#. Depth matters more than breadth.\r\nSoftware Design: Solid grasp of OOP, SOLID principles, design patterns, and how to make architectural decisions with long-term maintainability in mind.\r\nDatabases: Confident with relational databases (PostgreSQL, MySQL) — schema design, indexing, query optimization, and transactions. Working knowledge of at least one NoSQL store (MongoDB, Redis, DynamoDB).\r\nAPIs & Integration: Experience designing and consuming RESTful APIs; comfortable reading and writing service contracts and integration documentation.\r\nTesting: Writes meaningful unit, integration, and end-to-end tests — not for coverage metrics, but for genuine confidence in your code.\r\nCloud & DevOps: Familiar with at least one major cloud platform (AWS, GCP, Azure), Docker, CI/CD pipelines, and basic infrastructure practices.\r\nVersion Control: Strong Git workflow: branching strategies, pull requests, and code review culture.\r\nAI Literacy — Expected & Growing\r\nAI Tool Adoption: Actively uses AI coding assistants in day-to-day development and can demonstrate concrete productivity or quality improvements as a result.\r\nLLM Integration: Has built or integrated at least one LLM-powered feature or workflow in a real project — even if exploratory or side-project experience counts.\r\nPrompt Awareness: Understands the basics of prompt design, few-shot examples, and how to get reliable, structured outputs from LLM APIs.\r\nCritical Evaluation: Applies engineering discipline to AI outputs — tests them, validates them, and knows when not to trust them.\r\nCuriosity: Genuinely interested in how AI tooling is evolving and proactively experiments with new approaches.\r\nPreferred Qualifications\r\nExperience building RAG pipelines or working with vector databases (Pinecone, pgvector, Weaviate, etc.).\r\nFamiliarity with AI frameworks such as LangChain, LlamaIndex, or similar orchestration tools.\r\nExposure to ML concepts: embeddings, model evaluation, fine-tuning, and working with data science teams.\r\nExperience with observability and monitoring tooling (Datadog, Grafana, OpenTelemetry, or equivalent).\r\nBackground in microservices architecture and event-driven systems (Kafka, RabbitMQ, etc.).\r\nKnowledge of security best practices: OWASP Top 10, authentication/authorization, input validation.\r\nExperience with Agile/Scrum methodologies and tools like Jira or Linear.\r\nOpen-source contributions or public portfolio demonstrating your engineering work.\r\nCore Competencies\r\nEngineering Craft: Clean, tested, maintainable code\r\nDatabase Acumen: SQL, NoSQL, schema & performance\r\nAI Fluency: Tools + LLMs as force multipliers\r\nPerformance Mindset: Measures, optimizes, validates\r\nQuality Discipline: Tests like production depends on it\r\nCross-Team Voice: Fluent in code AND in business\r\nSound Judgment: Knows when — and when NOT — to use AI\r\nOwnership Drive: Ships end-to-end, measures impact\r\nTechnology Landscape\r\nLanguages: Python, TypeScript, Java, Go, SQL\r\nDatabases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, pgvector\r\nCloud & Infra: AWS / GCP / Azure, Docker, Kubernetes, Terraform\r\nAPIs: REST, GraphQL, gRPC, OpenAPI / Swagger\r\nAI & LLM: OpenAI, Anthropic Claude, Gemini — integrated as features, not the foundation\r\nAI Dev Tools: GitHub Copilot, Cursor, Claude Code — used daily to accelerate engineering\r\nObservability: Datadog, OpenTelemetry, Prometheus, Grafana\r\nData: PostgreSQL, dbt, Airflow, Spark, Kafka\r\nCI/CD: GitHub Actions, ArgoCD, Jenkins, CircleCI\r\nEqual Opportunity Employer\r\nCarParts.com is an equal-opportunity employer. We enthusiastically accept our responsibility to make employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, national origin, religion, marital status, medical condition, physical or mental disability, military service, pregnancy, childbirth and related medical conditions, or any other classification protected by federal, state, and local laws and ordinances. Our management is dedicated to ensuring that we fulfill this policy with respect to hiring, placement, promotion, transfer, demotion, layoff, termination, recruitment advertising, pay, and other forms of compensation, training, and general treatment during employment.\r\nThe above-noted job description is not intended to describe, in detail, the multitude of tasks that may be assigned but rather to give the incumbent a general sense of the responsibilities and expectations of his/her position. As the nature of business demands change so, too, may the essential functions of this position.","company":"Carparts","rawCompany":"carparts","city":"Long Beach","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-03T02:09:07.295Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1254.00","title":"Web Developers","slug":"web-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Full Stack Developer / AI Focused","description":"Job Description Job Description About Us\r\nWe are a fast-growing technology company building products used by hundreds of thousands of customers, backed by a supply chain spanning multiple distribution centers and a data platform processing millions of events daily. Engineering is not a support function here — it is a core driver of every major business outcome.\r\nWe move fast, hold ourselves to a high bar, and believe the best engineering decisions are made by people who are close to the business. We are embracing AI not as a trend, but as a genuine multiplier — using it to ship better software, faster, while never losing sight of the craftsmanship that makes software great.\r\nThe Role\r\nThis is a forward-deployed engineering role with AI at its core. You will architect systems, write production-grade code, design databases, and own your projects end to end — with a primary focus on deploying AI capabilities directly into the business and products that serve real users. What defines this role is the expectation that you bring strong engineering fundamentals together with a hands-on, deployment-first mindset for AI-powered tools and workflows.\r\nYou are not required to be an AI researcher or an ML specialist. You are expected to be an excellent engineer who deploys AI where it creates real value — using it to accelerate delivery, build intelligent features, and solve hard problems, while applying your own judgment to validate, refine, and own the outcome.\r\nStrong Engineering Core\r\nDesign robust, scalable systems from the ground up\r\nWrite clean, well-tested, maintainable code\r\nOptimize database performance and data models\r\nDebug complex issues across the full stack\r\nOwn code quality through rigorous peer review\r\nDeliver reliable software with measurable outcomes\r\nAI as a Force Multiplier\r\nUse AI coding assistants to accelerate development\r\nLeverage LLMs to generate boilerplate, tests, and docs\r\nBuild AI-powered features where they add real user value\r\nApply AI to improve code review, debugging, and analysis\r\nEvaluate AI outputs critically — judgment still wins\r\nStay current and bring new AI tools to the team\r\nOur Culture\r\nWhat We Believe\r\nOutcomes over activity — we measure what ships and what works.\r\nSpeed is a feature — long approval chains kill great products.\r\nRadical candor — honest feedback is a form of respect.\r\nLearning is non-negotiable — every sprint is a chance to improve.\r\nNo politics, no silos — collaborate openly across every team.\r\nHow We Work\r\nFast-paced sprints with a strong bias toward shipping.\r\nEngineers own requirements, architecture, and roadmap input.\r\nAI tools are standard kit — we share what works.\r\nBlameless post-mortems — failure is a learning event.\r\nAsync-first with intentional synchronous collaboration.\r\nKey Responsibilities\r\nCore Software Engineering (50%)\r\nDesign, build, and maintain scalable, high-quality software systems and APIs that serve real users in production.\r\nWrite clean, well-structured code with appropriate test coverage — unit, integration, and end-to-end.\r\nArchitect and optimize relational and non-relational database schemas, queries, and data models for performance and reliability.\r\nConduct meaningful code reviews that improve team quality and share knowledge, not just catch syntax errors.\r\nDebug, profile, and resolve performance bottlenecks and production issues with urgency and rigor.\r\nContribute to technical architecture decisions — propose solutions, evaluate tradeoffs, and document outcomes.\r\nParticipate actively in Agile ceremonies: sprint planning, standups, retrospectives, and backlog refinement.\r\nAI-Forward Deployment (35%)\r\nDeploy AI solutions end-to-end — from identifying the right use case, to building and shipping LLM-powered features directly into products and internal workflows.\r\nIncorporate LLM APIs and AI frameworks into product features where they create genuine user value: search, summarization, recommendations, intelligent automation, and decision support.\r\nApply critical engineering judgment to evaluate, refine, and validate all AI-generated outputs before they reach production — you own the result, not just the prompt.\r\nUse AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a daily accelerator — and champion effective AI tool patterns and prompt strategies across the team.\r\nStay at the front edge of the AI tooling landscape — evaluate new models, frameworks, and techniques and bring back deployment-ready recommendations that move the business forward.\r\nCross-Team Collaboration & Communication (15%)\r\nPartner with Marketing, Customer Experience, Data Science, Merchandising, Warehouse Ops, and Finance to understand requirements and deliver technical solutions.\r\nTranslate technical concepts clearly for non-technical stakeholders — written documentation, presentations, and live discussions.\r\nPresent project outcomes and architectural decisions to senior leadership with confidence and clarity.\r\nContribute to a culture of knowledge sharing: write internal documentation, run team demos, and mentor peers.\r\nCross-Team Collaboration\r\nEngineering here is a visible, active partner across the business — not a back-room function. You will work directly with teams who depend on the systems and data you build. Strong communication and commercial awareness are just as important as great code.\r\nMarketing: Personalization, campaign analytics, A/B platforms\r\nCustomer Experience: AI support tools, self-service flows, CSAT pipelines\r\nData Science: Model integration, feature engineering, shared infra\r\nMerchandising: Pricing, inventory intelligence, catalog tooling\r\nWarehouse Ops: Fulfillment automation, routing, operational dashboards\r\nFinance & Ops: Cost models, reporting pipelines, forecasting\r\nSupply Chain: Vendor integrations, PO systems, logistics optimization\r\nProduct: Feature scoping, roadmap input, rapid prototyping\r\nSecurity: Secure design, data privacy, compliance tooling\r\nRequired Qualifications\r\nEngineering Fundamentals — Non-Negotiable\r\nExperience: 3–6 years of professional software engineering in a production environment, with a portfolio of real systems you have owned and shipped.\r\nLanguages: Strong proficiency in one or more of: Python, Java, TypeScript, Go, or C#. Depth matters more than breadth.\r\nSoftware Design: Solid grasp of OOP, SOLID principles, design patterns, and how to make architectural decisions with long-term maintainability in mind.\r\nDatabases: Confident with relational databases (PostgreSQL, MySQL) — schema design, indexing, query optimization, and transactions. Working knowledge of at least one NoSQL store (MongoDB, Redis, DynamoDB).\r\nAPIs & Integration: Experience designing and consuming RESTful APIs; comfortable reading and writing service contracts and integration documentation.\r\nTesting: Writes meaningful unit, integration, and end-to-end tests — not for coverage metrics, but for genuine confidence in your code.\r\nCloud & DevOps: Familiar with at least one major cloud platform (AWS, GCP, Azure), Docker, CI/CD pipelines, and basic infrastructure practices.\r\nVersion Control: Strong Git workflow: branching strategies, pull requests, and code review culture.\r\nAI Literacy — Expected & Growing\r\nAI Tool Adoption: Actively uses AI coding assistants in day-to-day development and can demonstrate concrete productivity or quality improvements as a result.\r\nLLM Integration: Has built or integrated at least one LLM-powered feature or workflow in a real project — even if exploratory or side-project experience counts.\r\nPrompt Awareness: Understands the basics of prompt design, few-shot examples, and how to get reliable, structured outputs from LLM APIs.\r\nCritical Evaluation: Applies engineering discipline to AI outputs — tests them, validates them, and knows when not to trust them.\r\nCuriosity: Genuinely interested in how AI tooling is evolving and proactively experiments with new approaches.\r\nPreferred Qualifications\r\nExperience building RAG pipelines or working with vector databases (Pinecone, pgvector, Weaviate, etc.).\r\nFamiliarity with AI frameworks such as LangChain, LlamaIndex, or similar orchestration tools.\r\nExposure to ML concepts: embeddings, model evaluation, fine-tuning, and working with data science teams.\r\nExperience with observability and monitoring tooling (Datadog, Grafana, OpenTelemetry, or equivalent).\r\nBackground in microservices architecture and event-driven systems (Kafka, RabbitMQ, etc.).\r\nKnowledge of security best practices: OWASP Top 10, authentication/authorization, input validation.\r\nExperience with Agile/Scrum methodologies and tools like Jira or Linear.\r\nOpen-source contributions or public portfolio demonstrating your engineering work.\r\nCore Competencies\r\nEngineering Craft: Clean, tested, maintainable code\r\nDatabase Acumen: SQL, NoSQL, schema & performance\r\nAI Fluency: Tools + LLMs as force multipliers\r\nPerformance Mindset: Measures, optimizes, validates\r\nQuality Discipline: Tests like production depends on it\r\nCross-Team Voice: Fluent in code AND in business\r\nSound Judgment: Knows when — and when NOT — to use AI\r\nOwnership Drive: Ships end-to-end, measures impact\r\nTechnology Landscape\r\nLanguages: Python, TypeScript, Java, Go, SQL\r\nDatabases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, pgvector\r\nCloud & Infra: AWS / GCP / Azure, Docker, Kubernetes, Terraform\r\nAPIs: REST, GraphQL, gRPC, OpenAPI / Swagger\r\nAI & LLM: OpenAI, Anthropic Claude, Gemini — integrated as features, not the foundation\r\nAI Dev Tools: GitHub Copilot, Cursor, Claude Code — used daily to accelerate engineering\r\nObservability: Datadog, OpenTelemetry, Prometheus, Grafana\r\nData: PostgreSQL, dbt, Airflow, Spark, Kafka\r\nCI/CD: GitHub Actions, ArgoCD, Jenkins, CircleCI\r\nEqual Opportunity Employer\r\nCarParts.com is an equal-opportunity employer. We enthusiastically accept our responsibility to make employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, national origin, religion, marital status, medical condition, physical or mental disability, military service, pregnancy, childbirth and related medical conditions, or any other classification protected by federal, state, and local laws and ordinances. Our management is dedicated to ensuring that we fulfill this policy with respect to hiring, placement, promotion, transfer, demotion, layoff, termination, recruitment advertising, pay, and other forms of compensation, training, and general treatment during employment.\r\nThe above-noted job description is not intended to describe, in detail, the multitude of tasks that may be assigned but rather to give the incumbent a general sense of the responsibilities and expectations of his/her position. As the nature of business demands change so, too, may the essential functions of this position.","datePosted":"2026-06-03T02:09:07.295Z","dateModified":"2026-06-03T02:09:07.295Z","hiringOrganization":{"@type":"Organization","name":"Carparts","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Long Beach","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"dc4f9b3f165d2e1215cc698c"},"url":"https://jobsearcher.com/jobs/dc4f9b3f165d2e1215cc698c"}}