15 hours ago
Hyderābād, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize Databricks-based AI/ML infrastructure for data, feature engineering, model training, experimentation, and production deployment workloads.
- Configure and manage Databricks workspaces, clusters, jobs, model registry, MLflow tracking, feature engineering workflows, and cloud storage and compute integrations.
- Develop and maintain code, notebooks, scripts, infrastructure-as-code, automation, monitoring, and deployment tooling.
- Implement deployment automation and CI/CD pipelines using Git, Databricks Asset Bundles, Terraform, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate ML pipelines, model-serving components, and data and feature pipelines with attention to reliability, security, cost, and scalability.
- Monitor platform, cluster, and job health and troubleshoot notebooks, jobs, compute clusters, libraries, storage access, networking, and model-serving layers.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models and pipelines into enterprise systems.
- Document reusable patterns, configuration standards, and operational runbooks for Databricks AI infrastructure.
Requirements
- At least five years of experience is stated for the project role; the qualifications also specify at least two years in AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering.
- Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required, along with 15 years of full-time education.
- At least two years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Hands-on experience with Databricks workspaces, clusters, jobs, notebooks, MLflow, model registry, feature engineering workflows, and cloud storage integrations.
- Experience with scalable data and ML pipelines, distributed processing, model-serving patterns, production MLOps, CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and monitoring tools.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, and observability practices.
- Ability to optimize clusters, jobs, and pipelines for performance, reliability, scalability, cost, and security.
- Understanding of experiment tracking, model registry, model deployment, monitoring, rollback, and the compute, storage, networking, security, and deployment foundations of AI workloads.
- Preferred qualifications include Databricks certifications, enterprise data and AI platform experience in regulated industries, LLM workflows, vector search, retrieval pipelines, feature stores, model optimization, GPU-backed training, Unity Catalog, data governance, FinOps, incident management, and production support.
Tech Stack
Categories
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.