16 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize Databricks-based AI/ML infrastructure for scalable data, feature engineering, model training, experimentation, and production deployment.
- Configure and manage Databricks workspaces, clusters, jobs, model registries, MLflow tracking, feature engineering workflows, and cloud storage and compute integrations.
- Write, review, and debug code, notebooks, scripts, and infrastructure-as-code for 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 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 issues involving notebooks, jobs, compute clusters, libraries, storage access, networking, and model serving.
- 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 runbooks for Databricks-based AI infrastructure.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 5 years of overall experience is required; the qualifications also specify at least 2 years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud and platform engineering solutions.
- At least 2 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 registries, feature engineering workflows, and cloud storage integrations.
- Experience designing or operating scalable data and ML pipelines, distributed processing workloads, model-serving patterns, and production ML operations.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational 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 MLOps patterns including experiment tracking, model registries, model deployment, monitoring, and rollback.
- Strong understanding of the compute, storage, networking, security, and deployment foundations required for AI workloads.
- Preferred qualifications include Databricks certifications, familiarity with large language model 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.