3 days ago
Bengaluru, IndiaSenior
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, serving endpoints, networking, storage integrations, performance, cost, scalability, security, and reliability.
- Evaluate existing and proposed environments, document architecture decisions and trade-offs, and recommend remediation and optimization actions.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, and performance improvement opportunities.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring, observability, SLAs, SLOs, alerting, incident response, and performance and cost tracking across infrastructure and MLOps.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, compliance, regulatory, and client standards.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, review designs and code, mentor engineers, and lead AI engineering workstreams.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- A minimum of 12 years of experience is required, including at least 4 years working with AI/ML infrastructure engineering or related large-scale enterprise or hyperscaler platforms.
- At least 4 years of experience with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell is required.
- Strong 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, high-throughput data access, governance, and secure cloud integrations is required.
- Experience with workflow and data pipeline tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling 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 multiple initiatives is required.
- Strong communication, collaboration, problem-solving, stakeholder alignment, and client-delivery skills are required.
- Preferred qualifications include Databricks certifications, regulated-industry experience, LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, low-latency 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.