Role Summary
We are looking for a Senior Agentic AI Lead/ Architect to design and lead the development of enterprise-scale Agentic AI and Generative AI platforms. The role will focus on architecting autonomous and semi-autonomous AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent orchestration frameworks, primarily deployed on AWS cloud infrastructure.
The ideal candidate will provide technical leadership across AI architecture, platform design, and solution strategy, while guiding engineering teams to build scalable, secure, and production-ready AI systems.
Key Responsibilities
AI Architecture & Platform Design
- Define end-to-end architecture for Agentic AI and GenAI platforms across enterprise use cases.
- Design multi-agent AI systems using LLMs, RAG pipelines, and orchestration frameworks.
- Architect scalable, secure, and cloud-native AI solutions on AWS.
AI Solution Engineering
- Design systems using AWS AI/ML services such as:
- Amazon Bedrock
- Amazon SageMaker
- AWS Lambda
- AWS Step Functions
- Amazon S3
- Integrate LLM applications, knowledge retrieval pipelines, and AI agents into enterprise platforms.
Technical Leadership
- Establish AI architecture standards, governance frameworks, and best practices.
- Guide AI/ML engineers and platform teams, reviewing architecture and design decisions.
- Ensure system scalability, performance, and operational reliability.
Strategy & Collaboration
- Collaborate with product leaders, engineering teams, and business stakeholders on AI strategy and roadmap.
- Support pre-sales engagements and technical solutioning for AI platform initiatives.
Required Skills & Experience
Experience
- 915 years of overall IT experience
- 5+ years in AI/ML architecture or advanced AI engineering roles
AI & GenAI Expertise
- Strong expertise in:
- Generative AI (GenAI) architectures
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Agentic AI design patterns
Cloud & Platform Engineering
- Hands-on experience with AWS AI/ML ecosystem:
- Amazon Bedrock
- Amazon SageMaker
- AWS Lambda
- AWS Step Functions
- Amazon S3
AI Frameworks
- Experience working with AI orchestration frameworks, such as:
- LangChain
- LangGraph
- CrewAI
AI Platform Engineering
- Understanding of:
- MLOps and AI lifecycle management
- AI governance and model monitoring
- Distributed system scalability
Key Attributes
- Strong system design and architecture thinking
- Comfortable operating in fast-evolving AI environments with high ambiguity
- High ownership, accountability, and engineering rigor
- Excellent communication and stakeholder management skills
Preferred Qualifications
- AWS AI/ML Certification or AWS Solutions Architect Certification
- Experience delivering enterprise-scale AI platforms
- Exposure to multi-cloud or hybrid AI architectures
