3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize Databricks-based AI/ML infrastructure for data, feature engineering, model training, experimentation, and production deployment workloads.
- Write, review, and debug code, notebooks, scripts, and infrastructure-as-code for infrastructure automation, monitoring, and deployment tooling.
- Configure and manage Databricks workspaces, clusters, jobs, model registries, MLflow tracking, feature engineering workflows, and cloud storage and compute integrations.
- Deploy and operate ML pipelines, model-serving components, and data and feature pipelines with attention to reliability, security, cost efficiency, and scalability.
- Support deployment automation and CI/CD pipelines using Git, Databricks Asset Bundles, Terraform, Docker, Kubernetes, and workflow orchestration tools.
- 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 runbooks for Databricks-based AI infrastructure.
Requirements
- A bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- A minimum of 5 years of experience is stated in the project requirements; the qualifications section also specifies at least 2 years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell is required.
- Hands-on experience with Databricks workspaces, clusters, jobs, notebooks, MLflow, model registries, feature engineering workflows, and cloud storage integrations is required.
- Experience with scalable data and ML pipelines, distributed processing, model-serving patterns, production ML operations, CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring is required.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, and observability practices is required.
- Understanding of AI/ML concepts, compute, storage, networking, security, deployment foundations, MLOps, experiment tracking, model deployment, monitoring, and rollback approaches is required.
- Databricks certification, experience with regulated-industry data and AI platforms, familiarity with LLM workflows, vector search, retrieval pipelines, feature stores, GPU-backed training, Unity Catalog, data governance, FinOps, incident management, and 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.