AI LLM Technology Architecture Manager

 About The Role  

Skill required: Tech for Operations - Artificial Intelligence (AI)

Designation: AI LLM Technology Architecture Manager

Qualifications:Any Graduation

Years of Experience:13 to 18 years
Accenture is a global professional services company with leading capabilities in digital, cloud and security.Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song"” all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 699,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.Visit us at www.accenture.com
What would you do? As a Seniority Level 7 Agentic Architect at Accenture"”equivalent to Manager"”you'll be responsible for leading the design and rollout of autonomous AI solutions built on agentic architectures to solve complex business problems. Your duties involve building scalable and intelligent agents powered by generative AI, integrating these systems with enterprise platforms, and working closely with teams across different disciplines. You'll ensure that your solutions support client objectives, manage deployments in dynamic environments, mentor junior colleagues, help shape strategic direction, and oversee projects to guarantee AI implementations are ethical, secure, and efficient.

What are we looking for? Your duties involve building scalable and intelligent agents powered by generative AI, integrating these systems with enterprise platforms, and working closely with teams across different disciplines. You'll ensure that your solutions support client objectives, manage deployments in dynamic environments, mentor junior colleagues, help shape strategic direction, and oversee projects to guarantee AI implementations are ethical, secure, and efficient. Strong analytical and problem-solving abilities. Excellent communication and leadership skills, with a proven track record of project ownership. Passion for innovation and staying current with evolving AI technologies.Bachelor s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field is required. Master s degree in Machine Learning, AI, or a quantitative discipline is preferred. Relevant certifications (e.g., AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, or equivalent) are considered advantageous.

Roles and Responsibilities:

Work with generative AI technologies, including large language models (LLMs), embeddings, retrieval-augmented generation (RAG), prompt engineering, and autonomous agents. Design and implement multi-agent systems to address complex, multi-step problems using collaborative approaches. Deploy scalable AI solutions leveraging leading cloud platforms such as AWS, Azure, and Google Cloud, integrating with APIs and external tools. Apply software engineering best practices, including version control (Git), CI/CD pipelines, and containerization with Docker and Kubernetes. Incorporate ethical AI principles, bias mitigation strategies, and security best practices into the design and deployment of autonomous systems. Lead AI projects through all phases:requirements gathering, architecture design, implementation, and performance optimization. Core Technical Skills 1.

???????? Software Architecture & System Design ????????????? Microservices and modular architecture ????????????? Event-driven and message-passing systems ????????????? Scalability, reliability, and fault tolerance design 2.

???????? AI & LLM Foundations ????????????? Understanding large language models (LLMs) and multimodal models ????????????? Prompt engineering and structured prompting Knowledge of fine-tuning, embeddings, and retrieval-augmented generation (RAG) 3.

Agentic Orchestration ????????????? Frameworks like LangChain, Crew AI, Semantic Kernel,Langgraph ??????????? Multi-agent coordination strategies (collaboration, delegation, planning, negotiation) ???????????? Workflow orchestration (DAGs, state machines, planners) 4.Tool Integration ??????? API design and integration for agent tooling ????????????? Connecting agents with databases, APIs, and enterprise systems ????????????? Knowledge of vector databases ( cloud or on prem) 5?????? Infrastructure & MLOps ??????? Deployment & Scaling ????????????? Cloud platforms (AWS, Azure, GCP) ????????????? Containerization & orchestration (Docker, Kubernetes) ????????????? Serverless and edge AI architectures 6.

???????? Monitoring & Observability ????????????? Logging, tracing, and monitoring agent behavior ????????????? Feedback loops for continuous improvement ????????????? Guardrails, evaluation frameworks, and human-in-the-loop systems
 Qualification Any Graduation