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.