Senior AI System Architect

Accenture•Charlotte, NC

2d•$50 - $60•Onsite

About The Position

Accenture Flex is hiring a Senior AI System Architect for local, fixed-duration, project-based work. The role is responsible for defining and evolving the end-to-end architecture of an enterprise AI platform, including platform strategy, application architecture, AI/LLM integration, agentic workflows, data architecture, security, scalability, and production engineering. The architect serves as the technical authority for areas including knowledge bases, RAG solutions, agent frameworks, MCP integrations, document and structured-data intelligence, model orchestration, and platform modernization. The position leads architecture and engineering across AI and agentic platforms, data and knowledge systems, and containerized infrastructure. It establishes technical standards and roadmaps, guides implementation and governance, and ensures alignment with enterprise standards, security controls, and regulatory requirements. The architect provides technical leadership to architects, engineers, data scientists, and product teams, and partners with business and risk stakeholders to translate objectives into secure, scalable solutions. The role requires working onsite with the client up to four days per week in Charlotte, North Carolina; Jersey City, New Jersey; or Plano, Texas. The client also requires five days away per quarter. Accenture Flex employees work in their local metro area onsite at the project, significantly reducing or eliminating travel demands.

Requirements

At least 10 years of software architecture and enterprise platform engineering experience.
At least 5 years of Python development experience.
At least 5 years designing AI/ML, GenAI, or advanced analytics platforms.
Associate's degree.
Willingness to work onsite with the client up to four days per week in Charlotte, NC; Jersey City, NJ; or Plano, TX.
Availability for five days away per quarter as required by the client.

Nice To Haves

Good communication and leadership skills.
Deep understanding of Generative AI and LLM architectures.
Understanding of Retrieval Augmented Generation (RAG), agentic AI frameworks such as LangGraph and LangChain, MCP, vector databases, and embeddings.
Understanding of distributed systems, microservices, cloud-native architecture, and OpenShift/Kubernetes.
Strong experience with Python, REST APIs, Oracle, and enterprise data platforms.
Experience with Redis and caching technologies.
Experience with CI/CD and DevSecOps practices.
Banking or finance domain knowledge and experience.

Responsibilities

Own the target-state architecture and technical roadmap for the AI platform.
Design scalable, resilient, and secure enterprise AI solutions.
Define reference architectures, standards, patterns, and best practices.
Lead architecture reviews, technology selection, and solution governance.
Drive modernization initiatives across AI, cloud-native, and data platforms.
Design solutions using LLMs, RAG, agentic AI, MCP, vector databases, and knowledge-management frameworks.
Architect multi-agent workflows for document intelligence, commentary generation, validation automation, and data analysis.
Define model integration strategies with enterprise AI services.
Guide implementation of prompt management, evaluation frameworks, model governance, and AI observability.
Define architectures for structured and unstructured data ingestion.
Design integrations with databases, Starburst, S3, FileNet, and enterprise knowledge repositories.
Architect metadata generation, chunking, embeddings, vector storage, and retrieval strategies.
Establish data quality, lineage, and governance standards.
Lead architecture for OpenShift-based deployments and containerized workloads.
Design scalable services using Redis, Oracle, NetApp S3, and related infrastructure components.
Establish high availability, disaster recovery, failover, and performance optimization strategies.
Drive automation using CI/CD pipelines and infrastructure-as-code approaches.
Ensure compliance with enterprise security standards and AI governance requirements.
Incorporate entitlement management and access-control frameworks.
Partner with risk, audit, and compliance stakeholders during architecture reviews and approvals.
Support model risk management and regulatory review activities.
Provide technical leadership to architects, engineers, data scientists, and product teams.
Partner with business stakeholders to translate strategic objectives into technical solutions.
Mentor engineers and establish architecture excellence practices.
Lead technology planning, capacity forecasting, and platform investment decisions.