17 hours ago
Pune, IndiaStaff+
Responsibilities
- Own the end-to-end architecture and design of Databricks AI/ML infrastructure, including workspaces, compute, distributed training, pipelines, model registries, feature engineering, and model serving.
- Design and optimize Databricks clusters, jobs, workflows, pipelines, serving endpoints, networking, storage integrations, and high-throughput data access.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments and reviews, identify risks and optimization opportunities, and recommend remediation actions.
- Define roadmap inputs, capacity-planning models, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices, including SLAs, SLOs, alerting, performance tracking, cost tracking, and continuous optimization.
- Integrate AI/ML systems into enterprise environments while maintaining interoperability, security, compliance, regulatory alignment, and client standards.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, lead workstreams, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum 12 years of experience is required.
- At least 4 years of experience building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model-deployment pipelines, or large-scale engineering solutions.
- At least 4 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform for deploying large-scale solutions.
- Strong understanding of production AI/ML workloads and their computing infrastructure requirements.
- Experience with data-pipeline and workflow-management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- 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 and training workloads, model-serving patterns, high-throughput data access, governance, and secure cloud integrations.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, DataOps, MLOps, observability, and incident response practices.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing multiple initiatives.
- Preferred qualifications include Databricks Machine Learning Professional or Data Engineer Professional certifications, regulated-industry experience, LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, low-latency model serving, model optimization, FinOps, and vendor collaboration.
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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.