AI Infrastructure Architect

About Accenture

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
• Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
• Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
• Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
• Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
• Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.
• Required Skills/ Experience
• Hands-on experience with Databricks workspaces, clusters, jobs, notebooks, MLflow, model registry, feature engineering workflows and cloud storage integrations.
• Experience designing or operating scalable data and ML pipelines, distributed processing workloads, model-serving patterns and production ML operations.
• Working knowledge of Python, SQL, Spark, Terraform/Databricks Asset Bundles, Git-based CI/CD and observability practices.
• Ability to optimize clusters, jobs and pipelines for performance, reliability, scalability, cost and security.
• Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring and rollback approaches.
• Good to Have Skills
• Databricks certification such as Databricks Data Engineer, Machine Learning Associate/Professional or related lakehouse credentials.
• Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where data/AI platforms must meet compliance, reliability and data-governance expectations.
• Familiarity with large language model workflows, vector search, retrieval pipelines, feature stores, model optimization or GPU-backed training patterns.
• Knowledge of Unity Catalog, data governance, FinOps practices, incident management and production support processes for enterprise AI platforms.
• 15 years full time education

Responsibilities

• Write, review and debug code, notebooks, scripts and infrastructure-as-code for Databricks AI/ML infrastructure, automation, monitoring and deployment tooling.
• Configure and manage Databricks workspaces, clusters, jobs, model registry, MLflow tracking, feature engineering workflows and integrations with cloud storage and compute services.
• Support deployment automation and CI/CD pipelines for Databricks workloads using tools such as Git, Databricks Asset Bundles, Terraform, Docker, Kubernetes and workflow orchestration tooling where applicable.
• Deploy and operate ML pipelines, model-serving components and data/feature pipelines while applying reliability, security, cost-efficiency and scalability practices.
• Monitor platform, cluster and job health troubleshoot issues across notebooks, jobs, compute clusters, libraries, storage access, networking and model-serving layers.
• Collaborate with data scientists, ML engineers, platform engineers and architects to integrate Databricks AI models and pipelines into enterprise systems while meeting compliance and operational requirements.
• Document reusable patterns, configuration standards and runbooks for Databricks-based AI infrastructure.