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 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
This position is open to a principal architect who brings mastery across AEM platform architecture and enterprise AI systems. The requirement is for a technical authority who defines the AEM and AI strategy that entire programmes are built upon — governing cross-programme agentic content platform architecture, multi-LLM strategy, AI API integration standards, and the engineering culture of the entire AEM and AI practice.
The AI Native AEM Architect (Agentic/Applied) is required to define enterprise Content Intelligence platform architecture, govern LLMOps and AI safety frameworks across programmes, and shape the AEM and AI accelerator portfolio and engineering community.
Roles & Responsibilities:
a. Architecture Ownership: Define, govern, and evolve the end-to-end AEM solution architecture — component libraries, content models, MSM topology, and integration design use AI to accelerate documentation, validate decisions, and model trade-offs
b. AI Development Standards: Establish team-level standards for AI-assisted AEM delivery — prompt libraries, code review workflows for AI-generated output, quality gates, and responsible use guidelines
c. Requirements & Solution Design: Lead senior-level requirement analysis with client technology leadership use AI to synthesize complex requirement sets and generate solution proposals — then validate, refine, and own the recommendation
d. Performance & Integration Architecture: Define architecture for complex AEM integrations and platform performance apply AI-assisted diagnostics to root-cause issues and translate findings into architectural guidance
e. AEMaaCS Architecture: Own AEMaaCS platform architecture — Oak index design, dispatcher strategy, RDE governance, Content Fragment APIs, and Cloud Manager pipeline orchestration across large delivery teams
f. EDS Architecture: Architect EDS and AEM Sites hybrid solutions — custom block standards, Universal Editor integration, Helix/Franklin architecture, and CDN-layer optimization strategy
g. LLM API & Agentic Architecture: Define vendor-agnostic LLM integration patterns with fallback routing and cost governance as standard design practice architect how AEM content APIs and pipelines connect to agentic systems
h. LLMOps at Programme Scale: Own AI observability and governance across the workstream — prompt versioning, eval strategy, safety monitoring, and cost controls across the team's AI toolchain
i. Client-Facing Architecture Leadership: Lead architecture design sessions, solution walkthroughs, and proof-of-concept delivery with client technology and business leadership build trust through transparency about trade-offs and delivery risk
j. AI Impact & Governance Reporting: Own the measurement framework for AEM delivery quality and AI integration ROI present program level findings — accuracy, latency, cost, and business impact — to senior client stakeholders
k. Reusable Patterns & Practice Standards: Shape and publish reusable AEM architecture patterns, AI-accelerated accelerators, and engineering standards that scale across engagements and reduce ramp-up time for future programs
Professional & Technical Skills:
a. AEM Architecture: Deep expertise in AEM Sites solution architecture — component design, content modelling, MSM topology, DAM integration, and multi-site/multi-region delivery
b. AEM Development: Proficiency in HTL (Sightly), Sling Models, OSGi/Felix, and Java — with the ability to personally validate and review complex implementation decisions
c. AEMaaCS: Deep proficiency in AEMaaCS — Oak index management, CTT migration, dispatcher configuration, cloud-native customization constraints, RDE, and Cloud Manager pipeline orchestration across large teams
d. AEM EDS: Proficiency in AEM Edge Delivery Services — custom block authoring, Helix/Franklin architecture, Universal Editor integration, performance optimization, and CDN strategy design
e. AI-Assisted Development Governance: Ability to define and enforce team-level AI development standards using GitHub Copilot and Claude — code review workflows, prompt reusability patterns, and secure coding guidelines for AI-generated code
f. LLM API Architecture: Production experience designing LLM API integration patterns — vendor-agnostic abstraction, multi-provider fallback routing, token governance, and cost management across OpenAI, Anthropic, Vertex AI
g. Agentic & RAG Architecture: Working knowledge of agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) and RAG pipeline design understanding of how AEM content architecture supports AI agent pipelines
h. LLMOps: Proficiency in LLMOps at programme scale — eval harness design, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), safety monitoring, and cost governance
i. Cloud & DevOps: Cloud-native maturity: Kubernetes, Docker, microservices, serverless, CI/CD pipelines
j. Programming Languages: Strong Java and JavaScript proficiency in HTL (Sightly) Python
k. Adobe Platform Developers APIs (AEM, Firefly, AEP, AJO, etc)
Additional Information:
a. Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
b. 12+ years of commercial AEM development and architecture experience in production environments
c. Minimum 1 year of hands-on experience designing and deploying AI-integrated or agentic solutions in production — demonstrable through specific architectural decisions, not passive exposure
d. Demonstrated experience leading cross-team technical delivery with full architectural accountability
e. A 15-year full-time education background is required
Pune
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