AI Infrastructure Architect
Accenture
- Location
- Onsite (Bengaluru, Karnataka)
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the role
Accenture is seeking an AI Infrastructure Architect to define the vision and reference architecture for large-scale Databricks-based AI/ML and lakehouse systems. This role involves leading technical advisory, architecture assessments, and mentoring teams to deliver resilient, scalable production-ready AI infrastructure.
Skills
Databricks
AI Infrastructure
Machine Learning
Python
Spark
Unity Catalog
MLflow
Delta Lake
Cloud Architecture
Terraform
CI/CD
Data Governance
Model Serving
Distributed Systems
LLMOps
Infrastructure as Code
Full job details
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 15 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.
Key Responsibilities
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.
Required Qualifications
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 infrastructure projects and teams, including technical direction, design reviews, delivery governance and stakeholder alignment.
Strong project management, communication, problem-solving and cross-functional collaboration skills in fast-paced client or enterprise environments.
Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures and platform services for production solutions.
Required Skills/ Experience
Expert-level hands-on architecture experience with Databricks workspaces, clusters/serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Technical Architecting and model-serving capabilities.
Deep knowledge of Spark-based distributed processing, training/model pipelines, lakehouse architecture, model deployment, data governance, observability and resilience Technical Architecting.
Strong experience with Python, SQL, Spark, Terraform/Databricks Asset Bundles, Git-based CI/CD, security guardrails, monitoring and platform cost optimization.
Ability to evaluate multiple Databricks architecture options and produce standards, patterns, decision records, benchmarks and executive-ready recommendations.
Experience applying MLOps/DataOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response and rollback strategies.
Good to Have Skills
Databricks certifications such as Databricks Machine Learning Professional, Data Technical Architect Professional or related lakehouse architecture credentials.
Industry experience designing lakehouse and AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments with compliance, security and reliability constraints.
Exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed model training, model optimization and low-latency model serving.
Experience with Unity Catalog governance, enterprise architecture roadmaps, vendor/partner management, FinOps and production support operating models.
15 years full time education
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 15 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.
Key Responsibilities
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.
Required Qualifications
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 infrastructure projects and teams, including technical direction, design reviews, delivery governance and stakeholder alignment.
Strong project management, communication, problem-solving and cross-functional collaboration skills in fast-paced client or enterprise environments.
Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures and platform services for production solutions.
Required Skills/ Experience
Expert-level hands-on architecture experience with Databricks workspaces, clusters/serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Technical Architecting and model-serving capabilities.
Deep knowledge of Spark-based distributed processing, training/model pipelines, lakehouse architecture, model deployment, data governance, observability and resilience Technical Architecting.
Strong experience with Python, SQL, Spark, Terraform/Databricks Asset Bundles, Git-based CI/CD, security guardrails, monitoring and platform cost optimization.
Ability to evaluate multiple Databricks architecture options and produce standards, patterns, decision records, benchmarks and executive-ready recommendations.
Experience applying MLOps/DataOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response and rollback strategies.
Good to Have Skills
Databricks certifications such as Databricks Machine Learning Professional, Data Technical Architect Professional or related lakehouse architecture credentials.
Industry experience designing lakehouse and AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments with compliance, security and reliability constraints.
Exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed model training, model optimization and low-latency model serving.
Experience with Unity Catalog governance, enterprise architecture roadmaps, vendor/partner management, FinOps and production support operating models.
15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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