AI/ML Developer

As an experienced AI Architect, your role will involve the following key responsibilities: - Define integration strategies across LLMs, knowledge sources, tools, and enterprise systems to enable intelligent automation. - Architect multi-agent systems and orchestration frameworks for complex business workflows. - Enable seamless integration of AI capabilities with enterprise applications, platforms, and services. - Establish scalability, performance, and reliability guardrails for AI platforms and workflows. - Define architectural standards for high availability, fault tolerance, and low-latency processing. - Ensure AI systems can scale across multiple business units and enterprise workloads. - Enforce security, governance, and compliance standards aligned to enterprise policies and regulatory requirements. - Define frameworks for responsible AI, model explainability, data privacy, and auditability. - Align AI architecture with risk controls, zero-trust principles, and enterprise security posture. - Define architecture for model lifecycle management, CI/CD pipelines, and MLOps/ModelOps frameworks. - Ensure integration with deployment pipelines, monitoring systems, and observability platforms. - Promote adoption of cloud-native and containerized architectures. - Align AI architecture with enterprise roadmap, business priorities, and long-term transformation goals. - Evaluate emerging technologies and guide strategic investments in AI platforms and tools. - Mentor engineers and architects; drive architecture governance and design reviews (ARB/PTB alignment). You will play a crucial role in shaping the AI architecture and ensuring alignment with strategic business goals. As an experienced AI Architect, your role will involve the following key responsibilities: - Define integration strategies across LLMs, knowledge sources, tools, and enterprise systems to enable intelligent automation. - Architect multi-agent systems and orchestration frameworks for complex business workflows. - Enable seamless integration of AI capabilities with enterprise applications, platforms, and services. - Establish scalability, performance, and reliability guardrails for AI platforms and workflows. - Define architectural standards for high availability, fault tolerance, and low-latency processing. - Ensure AI systems can scale across multiple business units and enterprise workloads. - Enforce security, governance, and compliance standards aligned to enterprise policies and regulatory requirements. - Define frameworks for responsible AI, model explainability, data privacy, and auditability. - Align AI architecture with risk controls, zero-trust principles, and enterprise security posture. - Define architecture for model lifecycle management, CI/CD pipelines, and MLOps/ModelOps frameworks. - Ensure integration with deployment pipelines, monitoring systems, and observability platforms. - Promote adoption of cloud-native and containerized architectures. - Align AI architecture with enterprise roadmap, business priorities, and long-term transformation goals. - Evaluate emerging technologies and guide strategic investments in AI platforms and tools. - Mentor engineers and architects; drive architecture governance and design reviews (ARB/PTB alignment). You will play a crucial role in shaping the AI architecture and ensuring alignment with strategic business goals.