Custom Software Engineer

Project Role : Custom Software Engineer Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. Must have skills : Adobe Experience Manager (AEM) Sites Good to have skills : NA Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education

Summary: As an AEM AI-Native Engineer, requirement is to lead a team of AEM engineers while remaining deeply hands-on. Analyze requirements, propose solutions, help designing architecture, and write production-grade AEM code — while holding accountability for team's delivery outcomes and quality standards. Set up the AI-native engineering standard for the team and actively model it. From requirement analysis through to deployment and observability set the AI-native engineering standard and actively model it. Participate in technical design sessions, mentor junior AEM engineers, embed directly with client engineering teams, and build reusable AI-accelerated AEM patterns that scale beyond individual engagements.

Roles & Responsibilities: a. Requirement Analysis & Solution Design: Lead client requirement sessions use AI to synthesize briefs, surface gaps, and draft technical specifications — then validate the solution proposal b. Architecture & Component Development: Expected to be an SME and build complex components using Sling Models, OSGi, HTL, and Java use AI to accelerate pattern-based development, with full review of all AI-generated output c. Testing, Quality & Prompt Engineering: Define the team's testing strategy implement AI-generated test suites and automated quality gates maintain a shared library of AEM-specific prompts for component generation, OSGi config, and code review d. Integration & Performance: Involve in the AEM integrations with third-party APIs, DAM, and CRM systems diagnose and resolve complex performance issues using AI-assisted observability tooling e. AEMaaCS Delivery: Cloud Manager pipeline governance, Oak index management, dispatcher configuration, and cloud-native OSGi patterns use AI to generate deployment runbooks and environment checklists f. EDS: Guide the team on Edge Delivery Services block development and Helix/Franklin authoring leverage AI to accelerate block scaffolding and performance analysis g. LLM API & LLMOps: Knowledge of LLM APIs Integration with AEM-adjacent application layers manage token limits, latency, and cost trade-offs apply prompt versioning, AI output observability, and cost monitoring across the team's AI toolchain h. Team Management & Mentoring: Lead and coordinate the AEM team own sprint velocity, quality, and delivery milestones mentor junior engineers on AEM best practices and AI-native habits — model the standard, don't just set it i. AI Tooling Standards: Define team-level standards for AI-assisted development — prompt libraries, code review workflows for AI-generated code, and quality gates j. Client Engagement: Lead technical design sessions, architecture walkthroughs, and code-with sessions with client engineering teams build trust through transparency about trade-offs and AI tool limitations

Professional & Technical Skills: a. AEM Core: Deep proficiency in AEM Sites — component development, content modelling, MSM, DAM integration, and workflow design b. AEM Development: Hands-on expertise with HTL (Sightly), Sling Models, OSGi/Felix, and Java in production-grade delivery c. AEMaaCS: Working experience with Cloud Manager CI/CD pipelines, content versioning, and cloud-native OSGi development patterns d. AEM EDS: Working knowledge of EDS block development, Franklin/Helix authoring, and performance-first web delivery principles e. AI-Assisted Development: Experience setting team-level AI standards proficient use of GitHub Copilot and Claude for code generation, architecture assistance, test authoring, and AEM documentation f. Agentic System: Orchestration frameworks (LangGraph, LangChain, CrewAI) and RAG pipelines understanding of how AEM content APIs connect to agent-based architectures g. Programming Languages: Strong Java and JavaScript proficiency in HTL (Sightly) Python h. Adobe Platform Developers APIs (AEM, Firefly, AEP, AJO, etc) i. Cloud & DevOps: Kubernetes, Docker, microservices, CI/CD pipelines j. Agile: Experience leading Agile teams confident running sprint ceremonies with workstream-level delivery accountability

Additional Information: a. Bachelor s degree in computer science, Computer Engineering, Software Engineering, or a related field b. 5+ years of AEM development experience in production environments c. Minimum 1 year of hands-on AI-assisted development in a production AEM delivery context — demonstrable through specific examples, not passive exposure d. Demonstrated team lead or senior individual contributor experience with delivery accountability alongside hands-on technical contribution e. A 15-year full-time education background is required

15 years full time education

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