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



What would you do
As a Seniority Level 7 Agentic Architect at Accentureequivalent to Manageryoull 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. Youll 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. Youll 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) 5Infrastructure 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
QualificationAny Graduation