Must Have skills and Experience:
- Min 8 years proven experience with AI/ML LLMs Agentic workflows and enterprise integration tech stack with solution building
- Exposure of architecting complex AI or software systems at scale.
- Deep understanding of LLMs multi-agent systems planning algorithms and memory architectures.
- Proficiency in relevant languages and latest frameworks (e.g. Python TensorFlow/PyTorch LangChain AgentGPT or similar).
- Strong systems thinking and ability to lead architectural decisions across full AI stack.
- Strong understanding of API-first architecture integration patterns (REST GraphQL gRPC event-driven streaming) and enterprise middleware.
- Hands-on experience with cloud-native platforms (Azure/AWS/GCP) microservices containers (Docker/Kubernetes) and distributed systems.
- Demonstrated ability to guide and mentor technical teams.
Key Skills and Responsibilities:
- Tech Stack Leadership: Define and evolve the technology stack for agent orchestration planning tool use and long-term memory systems.
- Architect Agentic Systems: Design modular scalable and robust AI architectures that support autonomy memory reasoning and multi-agent collaboration.
- Define best practices for API gateways identity/authentication rate limiting observability and monitoring.
- Cross-functional Collaboration: Partner with ML engineers product managers and domain experts to align architecture with business and user needs.
- Innovation & Research: Stay abreast of the latest advances in agentic AI LLMs reinforcement learning neuro-symbolic methods and real-time systems.