3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define Databricks AI infrastructure vision, strategy, standards, and reference architectures for large-scale AI/ML and lakehouse systems.
- Own architecture decisions involving workspaces, clusters, serverless compute, jobs, storage, orchestration, model serving, governance, and cloud integrations.
- Prototype and benchmark cost-optimized distributed training, feature engineering, and model-serving environments.
- Define infrastructure-as-code, deployment, monitoring, SLA/SLO, security, and cost/performance governance patterns.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate Databricks, lakehouse, vector search, LLMOps, and model-serving capabilities for enterprise adoption.
- Provide executive and client-level technical advisory tied to business outcomes.
- Lead and mentor architects, establish best practices, and represent the practice in technical forums.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field is required.
- The posting states a minimum of 15 years of experience is required, while the detailed qualifications specify at least 6 years working with AI/ML infrastructure, cloud or data platforms, deployment pipelines, or large-scale architecture solutions.
- At least 6 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell is specified.
- Expert hands-on experience with Databricks workspaces, clusters or serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Delta Lake, feature engineering, and model serving is required.
- Strong knowledge of Spark-based distributed processing, training and model pipelines, lakehouse architecture, data governance, observability, resilience, and model deployment is required.
- Experience with Apache Airflow, Kubeflow, or managed and platform-native workflow tooling is required.
- Strong experience with Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based deployment practices, security controls, monitoring, and platform cost optimization is required.
- Experience applying MLOps, DataOps, or InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response, and rollback is required.
- Experience leading AI infrastructure projects and teams, conducting design reviews, providing technical direction, and managing stakeholder alignment is required.
- Preferred qualifications include Databricks certifications, industry experience in regulated or enterprise sectors, LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, model optimization, low-latency serving, vendor management, FinOps, and production support experience.
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About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.