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 setting 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

📌 Adobe Experience Manager (AEM) Sites (Pune)
🏢 Accenture
📍 Pune