15 hours ago
Hyderābād, 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, 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 infrastructure roadmaps, capacity planning models, 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 and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, performance tracking, cost tracking, and incident response.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, compliance, regulatory, and client standards.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, lead engineering 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.
- A minimum of 12 years of experience is required for the 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.
- At least 4 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform for deploying large-scale solutions.
- Strong understanding of production AI/ML workloads and the computing infrastructure required to deploy, operate, and optimize them.
- 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, governance, and secure cloud integrations.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, DataOps, MLOps, observability, and incident response.
- Experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.
- Preferred qualifications include Databricks Machine Learning Professional or Data Engineer Professional certifications and related lakehouse architecture credentials.
- Preferred experience includes regulated-industry data and AI platforms, LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed training, low-latency model serving, model optimization, Unity Catalog governance, FinOps, vendor collaboration, and production support operating models.
- Strong problem-solving, communication, collaboration, and stakeholder-alignment skills are required.
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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.