15 hours ago
Hyderābād, IndiaStaff+
Responsibilities
- Own the end-to-end architecture and design of Databricks AI/ML infrastructure, including workspaces, clusters, serverless compute, distributed training, ML pipelines, model registries, feature engineering, and model-serving environments.
- Design and tune Databricks clusters, jobs, pipelines, serving endpoints, networking, compute selection, and high-throughput data access patterns.
- 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 bottlenecks, and recommend remediation and optimization actions.
- Define roadmap inputs, capacity planning models, scaling strategies, cost forecasts, and performance improvements for AI infrastructure.
- Design deployment, automation, and CI/CD strategies for production AI systems, models, data pipelines, and platform components.
- Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance tracking, and cost tracking.
- Integrate AI/ML systems into enterprise environments while maintaining interoperability, security, compliance, and regulatory alignment.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate requirements into architecture standards.
- Set technical direction, lead workstreams, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- A bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 12 years of overall professional 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, PowerShell, or equivalent engineering languages.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform for large-scale solution deployment.
- Strong understanding of AI/ML concepts and the infrastructure required for production AI workloads.
- 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, Databricks Asset Bundles, Git-based CI/CD, DataOps, MLOps, observability, and incident response practices.
- Experience leading AI projects or engineering workstreams, managing multiple initiatives, evaluating AI technologies and cloud services, and producing architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.
- 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, Unity Catalog governance, FinOps, and infrastructure 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.