Key Responsibilities

Customer Experience & Application Engineering

• Design, build, and maintain high-performing, accessible front-end applications using Next.js, React, and modern web architectures.
• Develop, scale, and optimize business-critical GraphQL and REST APIs deployed on cloud infrastructure.
• Design efficient relational data models in PostgreSQL and build data pipelines in BigQuery using Dataform.
• Deliver and deploy code through modern DevOps/CI pipelines targeting AWS and GCP (Cloud Run, GKE, GCE, Cloud Build).

GenAI, Automation & Internal Tooling

• Build and maintain Model Context Protocol (MCP) servers connecting internal enterprise data and SaaS tools (AWS, GCP, Atlassian) to LLM "agents".
• Develop internal tools and workflow applications to streamline operations for tech and other teams.

Quality, Reliability & Security

• Establish and enforce rigorous automated testing standards (unit, integration, and E2E testing).
• Maintain enterprise-grade monitoring, logging, and alerting using cloud-native tools to ensure high availability and performance.
• Participate in routine operational activities including logging, monitoring, incident triage, and proactive system maintenance.
• Adhere strictly to security best practices, ensuring zero public exposure of PII or sensitive customer data, and proactively remediate code vulnerabilities.

Required Skills & Experience

• Full Stack Experience: 3–5 years of web application development experience in commercial environments.
• Core Languages & Frameworks: Strong proficiency in JavaScript, TypeScript/Node.js, with hands-on experience using Next.js, GraphQL, and REST APIs.
• Database & Data Pipelines: Solid experience with relational databases (PostgreSQL), query optimization, data modeling, and constructing data pipelines in BigQuery via GCP Dataform.
• Cloud & DevOps: Proven experience deploying and operating services on AWS and GCP (Cloud Run, GKE, GCE, Cloud Build, IAM, Secrets Manager, monitoring).
• AI & LLM Integration: Exposure to large language models or GenAI (Vertex AI, Gemini, OpenAI, Anthropic) with hands-on experience building simple LLM-backed endpoints or apps (prompting, function calling, RAG).
• Model Context Protocol (MCP): Ability to build and maintain MCP servers connecting internal data and SaaS products (GCP, AWS, Atlassian) to LLM "agents".
• Security, Governance & Reliability: Deep awareness of PII, data residency, and compliance constraints (mindful of never exposing customer data publicly). Experience maintaining enterprise-level monitoring/alerting and remediating high-severity vulnerabilities across multiple repositories.

Desired skills and experience

• Domain Curiosity: Curiosity about product and business workflows, with the willingness to shadow and learn directly from internal teams (e.g., Editorial).
• AI Ownership Trajectory: Comfortable starting as a "regular" Full Stack developer, with the drive to take on increasing ownership of AI and agentic tooling work over time.
• Communication & Stakeholder Management: Clear communication and documentation habits, backed by strong analytical skills to explain AI behavior, risks, and limitations to non-technical stakeholders.
• Product Mindset: Cares deeply about adoption, usability, and measurable commercial/customer impact.
• Agility & Adaptability: Skilled at navigating shifting priorities; maintains a consistent development velocity for new features while proactively resolving high-priority production issues to ensure system stability.
• Tooling Proficiency: Ability to work effectively within the Atlassian suite of products (Jira, Confluence, Bitbucket).
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Key Responsibilities

Customer Experience & Application Engineering

• Design, build, and maintain high-performing, accessible front-end applications using Next.js, React, and modern web architectures.
• Develop, scale, and optimize business-critical GraphQL and REST APIs deployed on cloud infrastructure.
• Design efficient relational data models in PostgreSQL and build data pipelines in BigQuery using Dataform.
• Deliver and deploy code through modern DevOps/CI pipelines targeting AWS and GCP (Cloud Run, GKE, GCE, Cloud Build).

GenAI, Automation & Internal Tooling

• Build and maintain Model Context Protocol (MCP) servers connecting internal enterprise data and SaaS tools (AWS, GCP, Atlassian) to LLM "agents".
• Develop internal tools and workflow applications to streamline operations for tech and other teams.

Quality, Reliability & Security

• Establish and enforce rigorous automated testing standards (unit, integration, and E2E testing).
• Maintain enterprise-grade monitoring, logging, and alerting using cloud-native tools to ensure high availability and performance.
• Participate in routine operational activities including logging, monitoring, incident triage, and proactive system maintenance.
• Adhere strictly to security best practices, ensuring zero public exposure of PII or sensitive customer data, and proactively remediate code vulnerabilities.

Required Skills & Experience

• Full Stack Experience: 3–5 years of web application development experience in commercial environments.
• Core Languages & Frameworks: Strong proficiency in JavaScript, TypeScript/Node.js, with hands-on experience using Next.js, GraphQL, and REST APIs.
• Database & Data Pipelines: Solid experience with relational databases (PostgreSQL), query optimization, data modeling, and constructing data pipelines in BigQuery via GCP Dataform.
• Cloud & DevOps: Proven experience deploying and operating services on AWS and GCP (Cloud Run, GKE, GCE, Cloud Build, IAM, Secrets Manager, monitoring).
• AI & LLM Integration: Exposure to large language models or GenAI (Vertex AI, Gemini, OpenAI, Anthropic) with hands-on experience building simple LLM-backed endpoints or apps (prompting, function calling, RAG).
• Model Context Protocol (MCP): Ability to build and maintain MCP servers connecting internal data and SaaS products (GCP, AWS, Atlassian) to LLM "agents".
• Security, Governance & Reliability: Deep awareness of PII, data residency, and compliance constraints (mindful of never exposing customer data publicly). Experience maintaining enterprise-level monitoring/alerting and remediating high-severity vulnerabilities across multiple repositories.

Desired skills and experience

• Domain Curiosity: Curiosity about product and business workflows, with the willingness to shadow and learn directly from internal teams (e.g., Editorial).
• AI Ownership Trajectory: Comfortable starting as a "regular" Full Stack developer, with the drive to take on increasing ownership of AI and agentic tooling work over time.
• Communication & Stakeholder Management: Clear communication and documentation habits, backed by strong analytical skills to explain AI behavior, risks, and limitations to non-technical stakeholders.
• Product Mindset: Cares deeply about adoption, usability, and measurable commercial/customer impact.
• Agility & Adaptability: Skilled at navigating shifting priorities; maintains a consistent development velocity for new features while proactively resolving high-priority production issues to ensure system stability.
• Tooling Proficiency: Ability to work effectively within the Atlassian suite of products (Jira, Confluence, Bitbucket).
•