15 hours ago
Hyderābād, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize Databricks-based AI/ML infrastructure.
- Configure and manage Databricks workspaces, clusters, jobs, notebooks, model registries, MLflow tracking, feature engineering workflows, and cloud storage integrations.
- Develop and maintain code, notebooks, scripts, infrastructure-as-code, automation, monitoring, and deployment tooling.
- Build deployment automation and CI/CD pipelines using Git, Databricks Asset Bundles, Terraform, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate data pipelines, feature pipelines, ML pipelines, and model-serving components.
- Troubleshoot notebooks, jobs, compute clusters, libraries, storage access, networking, and model-serving layers.
- Optimize infrastructure and workloads for performance, reliability, scalability, security, and cost efficiency.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models and pipelines into enterprise systems.
- Document reusable configuration patterns, standards, and operational runbooks.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum five years of overall experience is required.
- At least two years of experience coding, building, monitoring, or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- 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 workloads, model-serving patterns, and production MLOps.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, and observability practices.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring tools.
- Understanding of AI/ML concepts, compute, storage, networking, security, deployment foundations, experiment tracking, model deployment, monitoring, and rollback approaches.
- Databricks certification, lakehouse credentials, regulated-industry experience, and familiarity with LLM workflows, vector search, retrieval pipelines, feature stores, model optimization, GPU-backed training, Unity Catalog, data governance, FinOps, incident management, or production support are preferred.
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.