3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Own 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 environments.
- Design and optimize Databricks clusters, jobs, workflows, pipelines, serving endpoints, networking, storage integrations, performance, scalability, security, reliability, and cost.
- Lead architecture assessments, reviews, technology evaluations, trade-off analyses, roadmap planning, capacity modeling, and remediation recommendations.
- Define deployment, automation, CI/CD, monitoring, observability, incident response, SLA/SLO, compliance, and production operating practices for AI systems and platform components.
- Integrate AI/ML systems into enterprise environments and collaborate with clients, architects, stakeholders, and engineering teams to translate requirements into architecture standards.
- Set technical direction, document architecture decisions and reusable patterns, review designs and code, and mentor engineers.
Requirements
- 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 required.
- At least 4 years of experience coding, 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 proficiency with programming or scripting 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 deploying large-scale solutions.
- 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.
- Experience with scalable data and ML pipelines, distributed processing or training, model-serving patterns, high-throughput data access, 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 architecture patterns, and managing priorities across multiple initiatives.
- Preferred qualifications include Databricks Machine Learning Professional or Data Engineer Professional certifications, regulated-industry experience, and exposure to 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.