Job Description
Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Cognitive Engineering
We are seeking a highly skilled and visionary AI Architect to design and lead the architecture of cutting-edge AI systems involving agent orchestration, LLM integration, and enterprise-scale deployment. You will play a pivotal role in selecting the right models, frameworks, and strategies to enable intelligent multi-agent ecosystems, integrating them seamlessly into existing enterprise environments.
Key Responsibilities:
Design the overall AI system architecture, focusing on agent orchestration, system integration, and end-to-end scalability.
Evaluate and select appropriate LLMs, orchestration frameworks, tools, and APIs aligned with use case complexity and enterprise constraints.
Architect knowledge management systems using RAG (Retrieval-Augmented Generation) and hybrid search patterns.
Enable plan-and-execute strategies and multi-agent coordination for complex task automation.
Ensure secure, cost-optimized design through API gateway integration, token & context-window budgeting, and resource utilization planning.
Drive Gen AI threat modeling, safety tier design, and defenses against adversarial prompts.
Collaborate with cross-functional teams to ensure seamless A2A (agent-to-agent) and tool-based orchestration.
Lead proof-of-concepts, technical assessments, and production readiness for agent-based applications.
Key Skills & Experience:
Strong understanding of architecture patterns including knowledge management (RAG, hybrid search), agent design, and enterprise AI.
Hands-on expertise with LLM orchestration frameworks such as LangGraph, CrewAI, AutoGen, Vertex AI, AI Refinery.
Proficiency in tools-as-agents, function calling, API integration, model context protocols (e.g., MCP), and A2A communication.
Experience with Gen AI safety design, including threat modeling and prompt protection strategies.
Knowledge of API gateway design, performance optimization, cost-aware system architecture, and context budgeting.
Experience in working across cloud-native and hybrid environments with secure enterprise integration patterns.
