LLM Full Stack Engineer Associate Manager

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at accenture.com.The AI Large Language Model (LLM) Technology Architecture Associate Manager is responsible for designing and evolving the enterprise Artificial Intelligence (AI) architecture blueprint—with a strong emphasis on Generative AI, Large Language Models (LLMs), and multimodal models—and building the technical capabilities required to operationalize AI at scale. The role spans technology evaluation and selection, reference architectures, implementation roadmaps, platform enablement across data, models, operations, security, and governance, and end-to-end observability and cost/performance optimization. You will lead multidisciplinary workstreams, align stakeholders, and guide solutions from Proof of Concept (POC) to production with robust Responsible AI practices.Key ResponsibilitiesEngage with clients and stakeholder teams owning source systems and data platforms to define integration strategies, align interfaces and data flows, and ensure seamless incorporation of enterprise assets into scalable AI foundation components and solutions.Define and evolve the enterprise AI architecture blueprint, including LLM and multimodal (text, vision, and audio) capabilities, ensuring alignment with business strategy and information technology standards.Assess, compare, and recommend technology options across data platforms, model development and serving, orchestration, observability, security, and governance; Capture choices made, why, and expected benefits and drawbacks.Develop implementation roadmaps to move from the current state to a scalable AI foundation, sequence capabilities, platforms, and integrations for progressive rollout.Build and operationalize end-to-end AI foundation components: data ingestion and curation, vector search and Retrieval-Augmented Generation (RAG) pipelines, model serving gateways, feature and context stores, prompt management and versioning, evaluation tools, and operations for large language model systems (LLMOps).Design and build custom architectural components where needed, such as model routing, safety and guardrail services, retrieval and reranking services, caching layers, and policy enforcement, to enable scale and reusability.Optimize our AI environment so models train and run fast, reliably, and cost effectively, meeting agreed performance and availability targets.Implement end-to-end AI governance with security and Responsible AI controls: data privacy and protection of personally identifiable information (PII), policy enforcement, identity and access control, content safety, model risk assessment, auditability, and continuous monitoring.Establish observability for AI systems: metrics, logs, tracing, telemetry, cost monitoring (including tokens and infrastructure), quality and safety metrics, drift detection, and incident response runbooks.Own technical workstreams, manage client expectations, and coordinate with platform, data, security, and product teams to deliver outcomes on time and within budget.Guide POCs to production-grade solutions, institutionalize best practices, and standardize patterns and reusable assets across teams.Mentor engineers and architects; present and defend architectural solutions and trade-offs to technical and executive audiences.SkillsAdvanced English skills: writing, reading, and speaking (top priority).Enterprise AI architecture for LLMs and multimodal models, including:Retrieval-Augmented Generation (RAG) architectures: chunking strategies, embeddings, reranking, grounding, and guardrails.Operations for large language model systems (LLMOps): prompt and template management with versioning, evaluation suites, A/B testing (controlled experiments comparing variants), canary releases, and monitoring.Model serving and routing across multiple models and providers.Proficiency in Python and experience building and integrating services and Application Programming Interfaces (APIs), for example with FastAPI or Flask; strong Object-Oriented Programming.Strong knowledge of server-side development in Java, .NET, Python, and Node.js, including microservices and integration patterns.Advanced knowledge of architectural patterns and Model-View-Controller (MVC) and other web frameworks; API design and lifecycle using Representational State Transfer (REST) and understanding of Simple Object Access Protocol (SOAP).Data platforms and storage:Relational databases using Structured Query Language (SQL) and non-relational (NoSQL) databases; vector databases such as PostgreSQL with pgvector, Pinecone, or Weaviate.Data ingestion and streaming (for example, Apache Kafka or Azure Event Hubs) and data lake or lakehouse concepts.Security and governance:Identity and access management, including OAuth 2.0 authorization and OpenID Connect, JSON Web Tokens (JWTs), secrets management, encryption, and network security.Responsible AI controls, content safety, personally identifiable information (PII) redaction, model risk management, and audit readiness.Performance and scalability:Horizontal and vertical scaling, caching strategies, concurrency optimization, profiling, and performance tuning.Compute optimization for AI serving, including batching, quantization, high-performance runtimes, latency reduction, and cost control.Development and operations (DevOps) and machine learning operations (MLOps):Continuous Integration and Continuous Delivery (CI/CD) and Continuous Training for machine learning; Infrastructure as Code (IaC) with tools such as Terraform or Bicep; containerization and orchestration with Docker and Kubernetes.Integration servers and pipelines; operations driven by Git workflows (GitOps); artifact and model registries.Observability:Metrics, logs, and traces (for example, OpenTelemetry); model quality and safety metrics; cost and capacity monitoring; alerting and service level objectives (SLOs).Documentation and design:Unified Modeling Language (UML), Context-Container-Component-Code (C4) model, sequence diagrams; Master Technical Documents; Architecture Decision Records (ADRs), Feasibility Matrix Stories.Cloud computing:Experience with Amazon Web Services (AWS), Microsoft Azure, and/or Google Cloud Platform (GCP) services for data, AI and machine learning, security, and operations.Front-end and integration awareness:Knowledge of HTML5, CSS3, JavaScript/TypeScript, ReactJS/NextJS/AngularJS, TailwindCSS and other frameworks for client-side integration scenarios.Required ExperienceAt least 7 years of proven experience as a software engineer or architect implementing distributed and web-based solutions (web sites, services, and APIs) in enterprise settings.Hands-on architecture and implementation of AI and Generative AI solutions (at least 2 years), including LLM-based applications, Retrieval-Augmented Generation (RAG), prompt engineering, and model fine-tuning or parameter-efficient techniques.Strong experience designing and operating services and APIs, understanding the stateless nature of Hypertext Transfer Protocol (HTTP), session management, cookies, and external storage.Proven ability to diagnose bottlenecks and scalability limits; drive performance optimizations and cost reductions.Experience integrating external data sources and services efficiently; making informed data storage and retrieval decisions.Demonstrated security-by-design: service authentication and authorization, data protection, and best practices for AI-specific risks such as prompt injection and data leakage.Experience configuring and implementing DevOps and MLOps workflows and tools; containers and virtualized environments; integration and build servers.Experience with agile delivery and active participation in team ceremonies; peer review methodologies and engineering standards.Experience creating architectural documentation, diagrams, and technical governance artifacts.What We OfferHealth and Life Insurance Accenture days, 3 additional vacation days On site doctor Birthday leave Internet reimbursement