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 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an AEP AI-Native Architect, the requirement is to own the delivery for Adobe Experience Platform across the program. Define the enterprise data architecture strategy, set engineering and AI-native standards, and govern the quality of AEP delivery — across multiple workstreams, teams, and client organizations. Strong knowledge of AEP OOTB Agents.
Deliver complex, multi-source, production-grade AEP programs and lead client relationships at senior technology and business leadership, shape cross-engagement practice standards, define reusable AEP and AI architectural patterns, and create an environment where engineering rigor and AI-native practice are both the norm.

Roles & Responsibilities:
a. Architecture Ownership & AI Standards: Define, govern, and evolve the enterprise AEP solution architecture — XDM schema strategy, identity resolution topology, Real-Time CDP design, segmentation architecture, and activation patterns establish and govern program-wide AI-assisted engineering standards including prompt frameworks, AI output quality gates, and responsible use policies
b. Enterprise Data Modelling & Integration Architecture: Define enterprise-scale data models, source-to-XDM mapping strategies, and integration architecture across CRM, CDP, analytics, data warehouses, and marketing platforms govern integration patterns across all delivery teams use AI to accelerate mapping validation and surface cross-system dependencies early
c. AJO Architecture & Personalization Strategy: Define scalable AJO program architecture — journey framework standards, decision rule governance, personalization engine integration, and channel orchestration use AI to validate journey branching complexity and surface edge cases before program wide deployment
d. Requirements, Solution Strategy & Governance: Lead solution strategy sessions with senior client technology and business leadership use AI to synthesize complex cross-platform requirements and generate architectural options — then validate, refine, own, and govern solution decision-making across all workstreams.
e. Enterprise Governance & Compliance Architecture: Define and own the program-level data governance framework — DULE policy architecture, consent management strategy, data lineage design, privacy-by-design principles, and regulatory compliance posture (GDPR, CCPA) use AI to identify governance gaps at scale
f. AI Observability, LLMOps & Platform Performance: Own AI observability and LLMOps governance across all AEP workstreams — prompt versioning, eval strategy, safety monitoring, and cost controls define AEP platform performance and scalability standards including ingestion throughput, Query Service governance, and journey execution observability
g. Senior Client Engagement & AI Impact Reporting: Lead architecture strategy sessions and executive solution walkthroughs with senior client technology and business leadership define and own the measurement framework for AEP delivery quality and AI integration ROI present program-level findings in clear business terms
h. Reusable Patterns & Practice Standards: Shape and publish reusable AEP architecture patterns, AI-accelerated accelerators, and engineering standards that scale across engagements contribute to cross-engagement practice development and reduce ramp-up time for future program


Professional & Technical Skills:
a. AEP Enterprise Architecture: Deep expertise in AEP solution architecture at program scale — XDM schema strategy, identity resolution topology, Real-Time CDP design, multi-source ingestion frameworks, and multi-region/multi-brand delivery
b. Data Ingestion & Pipeline Architecture: Expert-level knowledge of AEP batch and streaming ingestion architecture, source connector strategy, data flow governance, pipeline reliability patterns, and error handling design at enterprise scale
c. Segmentation & Real-Time CDP Architecture: Expert-level experience designing audience segmentation architecture, merge policy strategy, identity graph design, and activation workflow frameworks for enterprise Real-Time CDP programs
d. Adobe Journey Optimizer (AJO): Deep proficiency in AJO program architecture — journey framework design, decision rule governance, personalization engine integration, channel orchestration strategy, and suppression logic at scale
e. AEP Query Service: Expert-level proficiency in AEP Query Service — complex SQL architecture, query governance standards, performance optimization strategy, and dataset analysis at program scale
f. API & Integration Architecture: Expert-level experience designing AEP API integration architecture (Profile, Segmentation, Data Ingestion, Flow Service, Destinations) ability to define and govern integration patterns across multiple delivery teams and client technology stacks
g. Enterprise Governance & Privacy Architecture: Proficiency in designing enterprise data governance frameworks — DULE policy architecture, consent management strategy, data lineage design, and privacy-by-design principles for large-scale regulated data programs
h. AI-Assisted Development Governance: Ability to define and enforce program-level AI engineering standards — prompt engineering frameworks, AI output governance, quality gates for AI-generated AEP configurations, and responsible use policies across multiple teams
i. LLM API Architecture: Production experience designing LLM API integration patterns — vendor-agnostic abstraction, multi-provider fallback routing, token governance, latency and cost management across OpenAI, Anthropic, Vertex AI
j. Agentic & RAG Architecture: Working knowledge of agentic orchestration frameworks (LangGraph, LangChain, CrewAI) and RAG pipeline design ability to articulate how AEP data architecture, identity APIs, and activation pipelines connect to AI agent systems
k. LLMOps: Proficiency in LLMOps at program scale — eval harness design, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), safety monitoring, and cost governance across multiple AI-assisted workstreams
l. Cloud & DevOps: Cloud-native maturity: AWS, Azure, or GCP CI/CD pipelines, IaC (Terraform or equivalent), and data platform infrastructure design at enterprise scale
m. Programming Languages: Expert SQL strong JavaScript Python
n. Architecture-level experience with AEP Intelligent Services — Customer AI, Attribution AI, and AI Assistant integration into enterprise AEP programs
o. Enterprise MarTech ecosystem architecture — CDP, CRM, DMP, data warehouse, and ad platform connectivity with AEP at program scale
p. Data mesh architecture patterns and federated data governance for large enterprise data programs
q. Experience leading AEP platform migration programs — Adobe Analytics migration, on-premise CDP to AEP, or multi-brand AEP consolidation
r. Multi-LLM provider architecture in production — fallback routing, cost governance, and provider-agnostic abstraction layer design

Additional Information:
a. Bachelor's degree in Computer Science, Computer Engineering, Data Engineering, Software Engineering, or a related field
b. 12+ years of commercial AEP development and architecture experience in production environments
c. Minimum 1 year of hands-on experience designing and deploying AI-integrated or agentic solutions in a production context — demonstrable through specific architectural decisions, not passive exposure
d. Demonstrated experience leading program-level technical architecture with cross-team governance accountability
e. A 15-year full-time education background is required

15 years full time education

About Accenture

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