16 hours ago
Chennai, 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 tune Databricks clusters, jobs, workflows, serving endpoints, networking, and high-throughput data access integrations.
- Evaluate architecture alternatives and lead architecture assessments, reviews, remediation planning, and technical decision-making.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish MLOps and InfraOps monitoring, observability, SLAs, SLOs, alerting, performance tracking, and cost optimization practices.
- Integrate AI/ML systems into enterprise environments while addressing interoperability, security, compliance, regulatory alignment, and client standards.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate requirements into architecture standards.
- Set technical direction, 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 professional experience is stated as required for the project role.
- 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 is required.
- At least 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent engineering languages is required.
- At least 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform is required.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling is required.
- Hands-on experience with Databricks workspaces, clusters or serverless compute, Workflows, jobs, notebooks, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Engineering, and model serving is required.
- Experience with scalable data and ML pipelines, distributed processing or training, model-serving patterns, high-throughput data access, governance, and secure cloud integrations is required.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, DataOps, MLOps, observability, and incident response is required.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing priorities across initiatives is required.
- Preferred qualifications include Databricks certifications, regulated-industry experience, exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, low-latency model serving, model optimization, 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.