15 hours ago
Chennai, IndiaSenior
Responsibilities
- Own the end-to-end architecture and design of Databricks AI/ML infrastructure, including workspaces, compute, distributed training, ML pipelines, model registries, feature engineering, and model serving.
- Design and tune Databricks clusters, jobs, pipelines, workflows, serving endpoints, networking, storage integrations, and high-throughput data access.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, 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.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, compliance, regulatory, and client standards.
- Document architecture decisions, standards, runbooks, reference implementations, and reusable platform patterns.
- Set technical direction, review designs and code, mentor engineers, and collaborate with clients, architects, stakeholders, and engineering teams.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field, with 15 years of full-time education stated.
- Minimum 7.5 years of experience is stated for the project role; the qualifications also specify at least 4 years building, operating, and designing 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 solutions.
- Strong understanding of AI/ML concepts, production AI workload infrastructure, Databricks workspaces and services, scalable data and ML pipelines, distributed processing, model serving, governance, and secure cloud integrations.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong 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 cloud services, and managing priorities across 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, Unity Catalog governance, 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.