Computer Scientist 2 (frontend)

Adobe Adobe · Enterprise · Noida, India

This role is for a Computer Scientist 2 (Frontend) at Adobe, focusing on building new features and experiences for Adobe Express. The role involves full-stack development, including front-end (JavaScript, TypeScript, React), back-end (NodeJS, APIs), and cloud services (AWS). A significant part of the role involves integrating AI capabilities into product features and building agentic workflows for the SDLC, requiring careful design for safety, reliability, and performance.

What you'd actually do

  1. Building modern front-end, back-end, and end-to-end user journeys
  2. Handling upstream and downstream dependencies
  3. Architecting new iterations of critical components, building and optimizing for massive scale.
  4. Using libraries/frameworks for tracking events and analyzing tracked data and user journeys
  5. Building and supporting Micro-Services

Skills

Required

  • B.Tech/M.Tech from a premier institute with 7+ years of experience including a few years in technical leadership with high-performance engineering teams.
  • Deep expertise in advanced JavaScript and TypeScript, with a solid understanding of core web technologies and experience in building responsive, elegant, and scalable web applications using modern frameworks such as React, Vue, Angular, etc.
  • Strong understanding of HTML5 and CSS3 design principles and techniques.
  • Strong backends development experience, with NodeJS, REST APIs, ElasticSearch, Python, SQS, SNS, Step Functions, Kubernets, Elastic Containers or similar tech stacks.
  • Experience with Amazon Web Services, with knowledge of AWS Services like Autoscaling, ELB, ElastiCache, SQS, SNS, RDS, S3, Serverless Architecture, AWS Lambda, Amazon API Gateway, Amazon DynamoDB, etc.
  • Extensive Knowledge of Web Standards and modern browsers, both at their API level and their internals (JS engines, browser performance, reflow, repaint, shadow DOM, progressive rendering, Service Workers, CDNs, CSS resetting, normalizing, SCSS, etc.).
  • Proficiency in building and architecting web apps that seamlessly function across various browsers, including mobile, where distinct performance, resource constraints, and capabilities necessitate polyfills.
  • Knowledge of load optimization and cloud deployment strategies, complemented by CI/CD pipelines.
  • Familiarity with monitoring systems like Splunk, New Relic, Grafana etc.
  • Good knowledge of algorithms, data structures, and distributed system design/implementation, and ability to debug
  • Experience with unit, integration and end to end testing
  • Maintain and fix parts of production environment
  • Willingness to participate in an on-call rotation
  • Leverage modern AI tools to significantly improve development velocity and code quality.
  • Build agentic workflows for all SDLC flows - test plan generation, code generation, debugging, test automation and incident response post shipping
  • Integrate AI capabilities into product features while ensuring deterministic fallbacks, reliability, and performance constraints.
  • Design systems that safely incorporate probabilistic AI outputs into production-grade software

Nice to have

  • Vue
  • Angular
  • ElasticSearch
  • Python
  • SQS
  • SNS
  • Step Functions
  • Kubernets
  • Elastic Containers
  • Autoscaling
  • ELB
  • ElastiCache
  • RDS
  • S3
  • Serverless Architecture
  • AWS Lambda
  • Amazon API Gateway
  • Amazon DynamoDB
  • Service Workers
  • SCSS

What the JD emphasized

  • advanced JavaScript and TypeScript
  • NodeJS
  • CI/CD pipelines
  • Splunk, New Relic, Grafana
  • Build agentic workflows for all SDLC flows - test plan generation, code generation, debugging, test automation and incident response post shipping
  • Integrate AI capabilities into product features while ensuring deterministic fallbacks, reliability, and performance constraints.
  • Design systems that safely incorporate probabilistic AI outputs into production-grade software

Other signals

  • Build agentic workflows for all SDLC flows - test plan generation, code generation, debugging, test automation and incident response post shipping
  • Integrate AI capabilities into product features while ensuring deterministic fallbacks, reliability, and performance constraints.
  • Design systems that safely incorporate probabilistic AI outputs into production-grade software