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 Platform (AEP)
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an AEP AI-Native, requirement is to design end-to-end AEP solutions — spanning data architecture, identity strategy, segmentation design, journey orchestration, and integration patterns — and are responsible for the technical quality and coherence of those designs across the engagement.
Have deep AEP platform knowledge and a track record of production delivery. Shaping solution architecture for complex, multi-source enterprise data environments, while using AI as a core part of design, validate, and accelerate delivery. Define AI-assisted development standards for the teams working within the architecture and own the quality of the deliverables. Strong knowledge of AEP OOTB Agents.
Engage with client technology stakeholders, lead technical design sessions, contribute to cross-engagement practice standards.

Roles & Responsibilities:
a. Architecture Design & Ownership: Design end-to-end AEP solution architecture — XDM schema strategy, identity resolution topology, data ingestion patterns, segmentation frameworks, and Real-Time CDP activation design use AI to accelerate architecture documentation, validate design decisions, and model trade-offs across options
b. Data Modelling & Integration Architecture: Define enterprise-scale data models, source-to-XDM mapping strategies, and integration patterns across CRM, CDP, analytics, and marketing platforms use LLM APIs and AI-assisted tooling to accelerate mapping validation and detect structural issues early
c. AJO Journey Architecture: Design scalable AJO journey frameworks — trigger logic, decision rules, personalization strategy, and channel orchestration patterns use AI to generate journey test scenarios, validate branching logic, and surface edge cases before production deployment
d. AI-Integrated Design Standards & Quality: Establish and govern AI-assisted development standards across the AEP delivery team — prompt libraries, code and configuration review workflows, quality gates for AI-generated data models and segment logic, and responsible use guidelines personally validate all AI-generated architectural outputs
e. Requirements & Solution Proposals: Lead requirement analysis sessions with client data and technology stakeholders use AI to synthesize complex requirement sets, surface cross-platform dependencies, and generate solution options — then validate, refine, and own the architectural recommendation.
f. Governance Architecture: Define the data governance framework for the engagement — DULE policy design, consent management architecture, data lineage standards, and privacy-by-design principles use AI to identify governance gaps and generate policy documentation
g. Observability & Performance Architecture: Define observability standards for AEP pipelines, ingestion jobs, and journey executions apply AI-assisted diagnostics to root-cause complex platform issues and translate architectural findings into actionable remediation plans for delivery teams
h. Client-Facing Architecture Leadership: Lead architecture design sessions, solution walkthroughs, and data strategy workshops with client technology and business stakeholders build trust through transparency about AEP architectural trade-offs, AI tool limitations and delivery risk
i. Team Mentoring & AI Standards: Mentor AEP engineers on platform best practices and AI-native development habits define AI tooling standards and prompt engineering patterns that scale across the team model the AI-native standard in the delivery work
j. Stakeholder Communication & Delivery Leadership: Communicate architectural decisions and AI-assisted delivery impact clearly to engineering teams and client leadership contribute to Agile sprint governance build reusable AEP architecture patterns and accelerators that reduce ramp-up time across future engagements

Professional & Technical Skills:
a. AEP Architecture: Deep proficiency in AEP solution architecture — XDM schema strategy, identity resolution topology, Real-Time CDP design, data ingestion patterns, and multi-source integration at enterprise scale
b. Data Ingestion & Pipelines: Expert-level knowledge of AEP batch and streaming ingestion, source connectors, data flow configuration, pipeline governance, and error handling strategies
c. Segmentation & Real-Time CDP: Advanced expertise in audience segmentation, merge policy design, identity graph strategy, and activation workflow architecture in Real-Time CDP
d. Adobe Journey Optimizer (AJO): Deep proficiency in AJO journey architecture — trigger design, decision rule frameworks, personalization strategy, channel orchestration, and campaign governance at enterprise scale
e. AEP Query Service: Advanced proficiency in AEP Query Service — complex SQL workloads, dataset analysis, performance optimization, and query governance for delivery teams
f. API & Integration Architecture: Expert-level experience designing AEP API integration patterns (Profile, Segmentation, Data Ingestion, Flow Service) ability to define and govern integration architecture across delivery teams and client technology stacks
g. Data Governance & Privacy Architecture: Proficiency in designing AEP governance frameworks — DULE policy architecture, consent management design, data lineage, and privacy-by-design principles for enterprise data programs
h. AI-Assisted Development Governance: Ability to define and enforce team-level AI development standards — prompt engineering patterns, code and configuration review workflows, quality gates for AI-generated AEP outputs, and responsible use guidelines
i. LLM API Architecture: Production experience designing LLM API integration patterns in AEP-adjacent application layers — vendor-agnostic abstraction, token governance, latency and cost management
j. Agentic & RAG: Expert in agentic orchestration frameworks (LangChain, LangGraph) and RAG pipeline fundamentals understanding of how AEP data architecture and APIs connect to AI agent systems
k. Cloud & DevOps: Cloud-native maturity: AWS, Azure, or GCP CI/CD pipelines and data platform infrastructure design
l. Programming Languages: Advanced SQL strong JavaScript Python a plus
m. Experience with AEP Destinations — designing data activation architecture to advertising, analytics, and marketing platforms at enterprise scale
n. Architecture-level knowledge of Customer AI and Attribution AI within AEP's Intelligent Services
o. Experience with Adobe Analytics migration architecture and cross-platform data unification strategy
p. Familiarity with enterprise MarTech ecosystem integration — CDP, CRM, DMP, and data warehouse connectivity with AEP
q. Data mesh and data platform architecture patterns for large enterprise AEP programs

Additional Information:
a. Bachelor's degree in Computer Science, Computer Engineering, Data Engineering, Software Engineering, or a related field
b. 8+ years of AEP development and architecture experience in production environments
c. Minimum 1 year of hands-on experience designing AI-integrated solutions in a production AEP delivery context — demonstrable through specific architectural decisions, not passive exposure
d. Demonstrated experience leading technical design sessions and engaging directly with client technology stakeholders
e. A 15-year full-time education background is required

15 years full time education

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