24 days ago
Gurgaon, IndiaSenior
Responsibilities
- Own the end-to-end architecture and design of optimized Databricks AI/ML infrastructure, including workspaces, compute, distributed training, ML pipelines, model registries, feature engineering, and model serving.
- Design and tune Databricks clusters, jobs, workflows, pipelines, serving endpoints, networking, storage integrations, and high-throughput data access patterns.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments and reviews, identify risks and optimization opportunities, and recommend remediation 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 AI monitoring and observability practices, 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.
- Document architecture decisions, standards, reference implementations, runbooks, and reusable platform patterns.
- Set technical direction, lead engineering workstreams, review designs and code, mentor engineers, and collaborate with clients and stakeholders.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field, along with 15 years of full-time education.
- Minimum 7.5 years of professional experience is stated for the role.
- 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 experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
- Strong understanding of AI/ML concepts and the infrastructure required for production AI workloads.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- 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.
- Experience architecting scalable data and ML pipelines, distributed processing and training workloads, model-serving patterns, governance, and secure cloud integrations.
- Experience with architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.
- Proven experience leading AI projects or engineering workstreams and managing multiple initiatives.
- Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Preferred qualifications include Databricks 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 infrastructure partner or vendor collaboration.
- Strong problem-solving, communication, collaboration, and stakeholder alignment skills.
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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.