AI Infrastructure Architect

About Accenture

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
• Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions.
• Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run and optimize production AI workloads.
• Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent engineering languages.
• Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
• Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
• Excellent communication, collaboration and stakeholder alignment skills.
• Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
• Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
• Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services and architecture patterns.
• Required Skills/ Experience
• Strong hands-on experience with Databricks workspaces, clusters/serverless compute, Workflows, jobs, notebooks, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Engineering and model-serving capabilities.
• Experience architecting scalable data and ML pipelines, distributed processing/training workloads, model-serving patterns, high-throughput data access, governance and secure cloud integrations.
• Strong working knowledge of Python, SQL, Spark, Terraform/Databricks Asset Bundles, Git-based CI/CD, DataOps, MLOps, observability and incident response practices.
• Ability to optimize Databricks AI infrastructure for performance, cost, scalability, security, reliability and compliance.
• Experience producing architecture decision records, reference implementations, standards, runbooks and reusable platform patterns.
• Good to Have Skills
• Databricks certifications such as Machine Learning Professional, Data Engineer Professional or related lakehouse architecture credentials.
• Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments where data/AI platforms must meet compliance, security, reliability and cost-control requirements.
• Exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, low-latency model serving and model optimization techniques.
• Knowledge of Unity Catalog governance, FinOps, infrastructure partner/vendor collaboration and production support operating models.
• 15 years full time education

Responsibilities

• Own end-to-end architecture and design of optimized Databricks AI/ML infrastructure, including workspace architecture, cluster/serverless compute, distributed training, ML pipelines, model registry, feature engineering and model-serving environments.
• Design and tune scalable Databricks clusters, jobs, pipelines and serving endpoints using Databricks Workflows, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Engineering and cloud storage integrations, including compute selection, networking and high-throughput data access design.
• Serve as an authoritative AI infrastructure expert on Databricks, applying deep knowledge of lakehouse architecture, ML lifecycle services, governance, security and cost levers.
• Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.
• Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.
• Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.
• Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.
• Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.
• Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.
• Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.
• Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.
• Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.