Teclusion

10+ years of experience in data architecture, AI/ML platforms, or enterprise architecture. Deep understanding of datacenter infrastructure, including compute, storage, networking, virtualization, and cloud integration. Hands-on experience with AI/ML technologies (e.g., model training, inference, MLOps, vector databases, LLM platforms). Strong knowledge of cloud platforms (Azure, AWS, or GCP) and hybrid architectures. Expertise in data engineering, distributed systems, and modern data stack technologies. Proven ability to define and execute AI adoption strategies at enterprise scale Preferred Qualifications Experience with GPU clusters, HPC, or AI‑optimized datacenter design. Familiarity with AI governance, responsible AI frameworks, and regulatory considerations. Background in enterprise transformation, consulting, or large‑scale modernization programs. Certifications in cloud architecture, data engineering, or AI/ML Key Responsibilities Strategic Leadership Define and own the enterprise Data & AI architecture roadmap, aligning with business goals and long-term technology strategy. Develop a comprehensive AI adoption strategy, including platform selection, governance, security, and operational readiness. Lead architectural decisions for datacenter modernization, hybrid cloud integration, and AI‑optimized infrastructure. Architecture & Design Design scalable, secure, and high‑performance data platforms, including data lakes, warehouses, and real‑time pipelines. Architect AI/ML platforms supporting model development, training, deployment, and lifecycle management. Evaluate and integrate GPU/AI‑accelerated infrastructure, edge computing, and high‑density datacenter designs. Ensure architectural alignment with enterprise standards, compliance, and regulatory requirements. Technical Expertise & Governance Establish best practices for data governance, metadata management, lineage, and quality. Define reference architectures, patterns, and frameworks for AI workloads and data systems. Guide teams on MLOps, DataOps, and automation strategies to improve reliability and speed. Collaboration & Influence Work closely with engineering, operations, cybersecurity, and business teams to ensure cohesive execution. Provide technical leadership and mentorship to architects, data engineers, and AI practitioners. Communicate complex architectural concepts to executives and non‑technical stakeholders.