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for Machine Learning, Deep Learning, Generative AI, and Agentic AI systems\nOwn and govern the end-to-end AI/ML ecosystem, including data pipelines, feature stores, model training environments, inference layers, and monitoring systems\nDefine and institutionalize best practices for MLOps and LLMOps, at scale, including multi-environment deployments, governance, observability, cost optimization, and lifecycle management\nArchitect and oversee enterprise-grade Generative AI and Agentic AI platforms, including RAG architectures, multi-agent orchestration, tool integration, memory management, and guardrails\nProvide architectural oversight and technical direction across multiple teams, ensuring consistency, scalability, and reusability of AI solutions\nCollaborate with senior stakeholders (Product, Engineering, Data, Security, Governance) to translate business strategy into AI driven solution blueprints\nLead technology evaluations, define platform strategies, and guide adoption of emerging tools, frameworks, and AI capabilities\nEnsure compliance with AI governance frameworks, including security, privacy, ethical AI, and regulatory standards\nMentor Tech Leads and senior engineers, driving architectural maturity and capability building across the organization\nAct as a key contributor in Architecture Review Boards (ARB) and strategic decision-making forums\nPerson Specifications\nBachelor's degree in IT, Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related field\n8-10 years of professional experience in Data Science, AI, or ML, working in production-grade environments, with significant experience in solution architecture and enterprise-scale system design\nTechnical Expertise\nDeep expertise in Machine Learning and Deep Learning, including advanced model design, optimization, and large-scale deployment\nExtensive hands-on and architectural experience in Generative AI (LLMs, RAG pipelines, embeddings, fine-tuning, evaluation frameworks)\nStrong experience designing Agentic AI systems (multi-agent architectures, orchestration frameworks, tool ecosystems, autonomous decision-making)\nProven track record in implementing MLOps practices at scale (CI/CD for ML, automated pipelines, monitoring, retraining strategies)\nAdvanced expertise in LLMOps, including prompt lifecycle management, evaluation pipelines, guardrails, observability, latency, and cost optimization\nStrong programming skills in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)\nExperience with cloud platforms (AWS, Azure, or GCP) and cloud?native platforms & services (e.g., Copilot Studio, Bedrock, Vertex AI, Azure OpenAI)\nStrong understanding of data engineering and data platform architecture (ETL/ELT pipelines, feature stores, data lakes/warehouses)\nExperience with distributed systems, microservices, APIs, and event driven architectures in AI contexts\nLeadership and Architectural 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