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This role acts as a critical bridge between product strategy, customer requirements, and engineering execution, requiring a strong blend of AI expertise, system architecture, and leadership skills.\nThe Architect will be responsible for leading architecture definition, driving complex proof-of-concept initiatives, enabling multi-agent system design, and managing cross-functional engineering teams. The role demands deep experience in LLM ecosystems, cloud-native platforms, and scalable system design to deliver robust, production-grade AI platforms with measurable business outcomes.\nKey Responsibilities:Solution Architecture: Lead the design and development of scalable, secure, and high-performance architectures for Agentic AI platforms using Python and modern frameworks\nTechnical Standards & Deliverables: Define architecture patterns, engineering standards, and best practices for development, deployment, and system scalability\nCollaboration: Work closely with product managers, engineering teams, DevOps, and QA to align technical solutions with business requirements and ensure seamless system integration\nStakeholder Interaction: Lead and execute technical POCs for Agentic AI solutions, working with customer stakeholders to define success criteria, build tailored agent configurations, and demonstrate business impact\nAgent Orchestration: Architect and implement multi-agent 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capabilities\nDocumentation: Create and maintain comprehensive architectural and technical documentation\nRequired Skills & Qualifications:12+ years of experience in software engineering with strong expertise in Python and backend system development\nExtensive experience in designing scalable, secure, multi-tenant AI/ML platforms\nDeep expertise in LLMs (OpenAI, Gemini, Anthropic, Llama) and agentic AI systems\nHands-on experience with agent frameworks such as AutoGen, CrewAI, LangGraph, and LangChain ecosystem (LangChain, LangSmith, LangFlow)\nStrong experience building RAG-based systems and working with vector databases\nProficiency in Python ecosystem including PyTorch, Scikit-learn, LlamaIndex, and evaluation tools like DeepEval\nDeep understanding of LLM concepts (prompt engineering, fine-tuning, function/tool calling, RAG)\nStrong experience in microservices architecture, REST APIs, and event-driven systems\nExpertise in relational (PostgreSQL, MySQL) and NoSQL databases 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