The Role

This 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.

You 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.

Strong Engineering Core

• Design robust, scalable systems from the ground up
• Write clean, well-tested, maintainable code
• Optimize database performance and data models
• Debug complex issues across the full stack
• Own code quality through rigorous peer review
• Deliver reliable software with measurable outcomes

AI as a Force Multiplier

• Use AI coding assistants to accelerate development
• Leverage LLMs to generate boilerplate, tests, and docs
• Build AI-powered features where they add real user value
• Apply AI to improve code review, debugging, and analysis
• Evaluate AI outputs critically - judgment still wins
• Stay current and bring new AI tools to the team

Key Responsibilities

Core Software Engineering (50%)

• Design, build, and maintain scalable, high-quality software systems and APIs that serve real users in production.
• Write clean, well-structured code with appropriate test coverage - unit, integration, and end-to-end.
• Architect and optimize relational and non-relational database schemas, queries, and data models for performance and reliability.
• Conduct meaningful code reviews that improve team quality and share knowledge, not just catch syntax errors.
• Debug, profile, and resolve performance bottlenecks and production issues with urgency and rigor.
• Contribute to technical architecture decisions - propose solutions, evaluate tradeoffs, and document outcomes.
• Participate actively in Agile ceremonies: sprint planning, standups, retrospectives, and backlog refinement.

AI-Forward Deployment (35%)

• Deploy AI solutions end-to-end - from identifying the right use case, to building and shipping LLM-powered features directly into products and internal workflows.
• Incorporate LLM APIs and AI frameworks into product features where they create genuine user value: search, summarization, recommendations, intelligent automation, and decision support.
• Apply critical engineering judgment to evaluate, refine, and validate all AI-generated outputs before they reach production - you own the result, not just the prompt.
• Use AI coding assistants (GitHub Copilot, Cursor, Claude Code) as a daily accelerator - and champion effective AI tool patterns and prompt strategies across the team.
• Stay 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.

Cross-Team Collaboration & Communication (15%)

• Partner with Marketing, Customer Experience, Data Science, Merchandising, Warehouse Ops, and Finance to understand requirements and deliver technical solutions.
• Translate technical concepts clearly for non-technical stakeholders - written documentation, presentations, and live discussions.
• Present project outcomes and architectural decisions to senior leadership with confidence and clarity.
• Contribute to a culture of knowledge sharing: write internal documentation, run team demos, and mentor peers.

Cross-Team Collaboration

Engineering 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.

• Marketing: Personalization, campaign analytics, A/B platforms
• Customer Experience: AI support tools, self-service flows, CSAT pipelines
• Data Science: Model integration, feature engineering, shared infra
• Merchandising: Pricing, inventory intelligence, catalog tooling
• Warehouse Ops: Fulfillment automation, routing, operational dashboards
• Finance & Ops: Cost models, reporting pipelines, forecasting
• Supply Chain: Vendor integrations, PO systems, logistics optimization
• Product: Feature scoping, roadmap input, rapid prototyping
• Security: Secure design, data privacy, compliance tooling

Required Qualifications

Engineering Fundamentals - Non-Negotiable

• Experience: 3-6 years of professional software engineering in a production environment, with a portfolio of real systems you have owned and shipped.
• Languages: Strong proficiency in one or more of: Python, Java, TypeScript, Go, or C#. Depth matters more than breadth.
• Software Design: Solid grasp of OOP, SOLID principles, design patterns, and how to make architectural decisions with long-term maintainability in mind.
• Databases: Confident with relational databases (PostgreSQL, MySQL) - schema design, indexing, query optimization, and transactions. Working knowledge of at least one NoSQL store (MongoDB, Redis, DynamoDB).
• APIs & Integration: Experience designing and consuming RESTful APIs; comfortable reading and writing service contracts and integration documentation.
• Testing: Writes meaningful unit, integration, and end-to-end tests - not for coverage metrics, but for genuine confidence in your code.
• Cloud & DevOps: Familiar with at least one major cloud platform (AWS, GCP, Azure), Docker, CI/CD pipelines, and basic infrastructure practices.
• Version Control: Strong Git workflow: branching strategies, pull requests, and code review culture.

AI Literacy - Expected & Growing

• AI Tool Adoption: Actively uses AI coding assistants in day-to-day development and can demonstrate concrete productivity or quality improvements as a result.
• LLM 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.
• Prompt Awareness: Understands the basics of prompt design, few-shot examples, and how to get reliable, structured outputs from LLM APIs.
• Critical Evaluation: Applies engineering discipline to AI outputs - tests them, validates them, and knows when not to trust them.
• Curiosity: Genuinely interested in how AI tooling is evolving and proactively experiments with new approaches.

Preferred Qualifications

• Experience building RAG pipelines or working with vector databases (Pinecone, pgvector, Weaviate, etc.).
• Familiarity with AI frameworks such as LangChain, LlamaIndex, or similar orchestration tools.
• Exposure to ML concepts: embeddings, model evaluation, fine-tuning, and working with data science teams.
• Experience with observability and monitoring tooling (Datadog, Grafana, OpenTelemetry, or equivalent).
• Background in microservices architecture and event-driven systems (Kafka, RabbitMQ, etc.).
• Knowledge of security best practices: OWASP Top 10, authentication/authorization, input validation.
• Experience with Agile/Scrum methodologies and tools like Jira or Linear.
• Open-source contributions or public portfolio demonstrating your engineering work.

Core Competencies

• Engineering Craft: Clean, tested, maintainable code
• Database Acumen: SQL, NoSQL, schema & performance
• AI Fluency: Tools + LLMs as force multipliers
• Performance Mindset: Measures, optimizes, validates
• Quality Discipline: Tests like production depends on it
• Cross-Team Voice: Fluent in code AND in business
• Sound Judgment: Knows when - and when NOT - to use AI
• Ownership Drive: Ships end-to-end, measures impact

Technology Landscape

Languages: Python, TypeScript, Java, Go, SQL

Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, pgvector

Cloud & Infra: AWS / GCP / Azure, Docker, Kubernetes, Terraform

APIs: REST, GraphQL, gRPC, OpenAPI / Swagger

AI & LLM: OpenAI, Anthropic Claude, Gemini - integrated as features, not the foundation

AI Dev Tools: GitHub Copilot, Cursor, Claude Code - used daily to accelerate engineering

Observability: Datadog, OpenTelemetry, Prometheus, Grafana

Data: PostgreSQL, dbt, Airflow, Spark, Kafka

CI/CD: GitHub Actions, ArgoCD, Jenkins, CircleCI