AI Infrastructure Architect

Project Role : AI Infrastructure Architect Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration. Must have skills : AI Agents & Workflow Integration Good to have skills : Databricks Unified Data Analytics Platform Minimum 18 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent As an Technical Architect in AI Infrastructure Architecture, you will act as a senior technical authority for Databricks-based AI/ML and lakehouse infrastructure, shaping the technical vision, reference architecture, standards and implementation strategy for large-scale AI systems. You will evaluate complex choices across workspace architecture, compute clusters, model lifecycle, model serving, data/feature pipelines, governance, observability, security and cost optimization, while guiding senior and lead architects/Technical Architects to deliver resilient, scalable and production-ready AI infrastructure. You will bring industry experience across enterprise AI adoption, compliance, reliability, FinOps and platform modernization to help clients translate AI infrastructure trade-offs into measurable business value.

Set the overarching Databricks AI infrastructure vision, strategy and reference architecture for large-scale AI/ML and lakehouse systems, including workspace architecture, compute, storage, orchestration, model serving and observability. Own complex architectural decisions across Databricks workspaces, clusters/serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Feature Technical Architecting, Delta Lake and cloud integrations, rationalizing options against client standards and business objectives. Architect and prototype cost-optimized distributed training, feature Technical Architecting and model-serving environments, building benchmarks, proof-of-concepts and reusable implementation patterns. Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, ML pipeline deployment patterns, monitoring strategy, SLAs/SLOs and cost/performance governance for production AI/ML systems. Lead architecture assessments and design reviews validate findings through hands-on implementation, profiling, performance tuning and troubleshooting across jobs, clusters, libraries, storage, security and serving layers. Evaluate emerging Databricks, lakehouse, vector search, LLMOps and model-serving capabilities, and recommend where they belong in enterprise solutions. Provide executive and client-level technical advisory, translating platform trade-offs into clear, defensible recommendations connected to business outcomes. Mentor architects and Technical Architects, build community best practices and represent the practice in internal and external technical forums.

Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology or a related Technical Architecting field. Minimum 6 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale Technical Architecting solutions. Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration and deployment foundations required to run production AI systems. Minimum 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent Technical Architecting languages. Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling. Proven experience leading AI