3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Set the vision, strategy, and reference architecture for large-scale Databricks AI/ML and lakehouse infrastructure.
- Own architecture decisions covering workspaces, clusters, serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Delta Lake, feature engineering, model serving, 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, reliability, and cost/performance governance standards.
- Lead architecture assessments and design reviews, validating recommendations through hands-on implementation, profiling, troubleshooting, and performance tuning.
- Evaluate Databricks, lakehouse, vector search, LLMOps, and model-serving capabilities for enterprise adoption.
- Advise executives and clients on technical trade-offs and business outcomes.
- 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.
- At least 18 years of experience is stated for the project role; the detailed qualifications also require at least 6 years building, operating, and architecting AI/ML infrastructure or related large-scale systems.
- At least 6 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell is required.
- 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 AI/ML concepts, Spark-based distributed processing, lakehouse architecture, model deployment, data governance, observability, resilience, and cloud or data platforms is required.
- Experience with workflow and data pipeline tools such as Apache Airflow, Kubeflow, or managed orchestration services is required.
- Experience with Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based deployment workflows, monitoring, security guardrails, and platform cost optimization is required.
- Experience applying MLOps, DataOps, or InfraOps practices for experiment tracking, model registries, deployment automation, monitoring, incident response, and rollback strategies is required.
- Proven experience leading AI infrastructure projects and teams, conducting design reviews, providing technical direction, and aligning stakeholders is required.
- Preferred qualifications include Databricks certifications, industry experience in regulated sectors, exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, model optimization, low-latency serving, FinOps, and vendor or partner management.
- Strong project management, communication, problem-solving, and cross-functional collaboration skills are required.
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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.