1 day ago
Hyderābād, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize Databricks-based AI/ML infrastructure for scalable 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 integrations.
- Deploy and operate ML pipelines, model-serving components, and data and feature pipelines with focus on reliability, security, cost efficiency, and scalability.
- Build 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 operational runbooks for Databricks AI infrastructure.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 5 years of overall experience is required, including experience 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 is required.
- Hands-on experience with Databricks workspaces, clusters, jobs, notebooks, MLflow, model registries, feature engineering workflows, and cloud storage integrations.
- Experience with scalable data and ML pipelines, distributed processing workloads, model-serving patterns, and production ML operations.
- Working knowledge of Python, SQL, Spark, Terraform or Databricks Asset Bundles, Git-based CI/CD, and observability practices.
- Understanding of AI/ML concepts; compute, storage, networking, security, and deployment foundations; MLOps; experiment tracking; model deployment; monitoring; and rollback approaches.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring tools.
- Preferred qualifications include Databricks certifications, enterprise data governance and FinOps knowledge, incident management experience, production support experience, and familiarity with LLM workflows, vector search, retrieval pipelines, feature stores, model optimization, or GPU-backed training.
- Exposure to regulated or compliance-sensitive industries such as BFSI, healthcare, retail/e-commerce, telecommunications, manufacturing, or the public sector is preferred.
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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.