1 day ago
Hyderābād, IndiaSenior
Responsibilities
- Architect and build custom AI infrastructure and hardware solutions while optimizing performance, power consumption, cost, and scalability.
- Design, automate, monitor, and optimize Databricks-based AI/ML infrastructure for data, feature engineering, model training, experimentation, and production workloads.
- 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 reliability, security, cost-efficiency, and scalability practices.
- 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 experience is stated for the project role.
- At least 2 years of experience with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions is listed in the required qualifications.
- 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 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.
- Understanding of AI/ML concepts, compute, storage, networking, security, deployment foundations, MLOps, model monitoring, and rollback approaches.
- Ability to optimize clusters, jobs, and pipelines for performance, reliability, scalability, cost, and security.
- Preferred qualifications include Databricks certifications, experience with regulated or compliance-sensitive industry use cases, LLM workflows, vector search, retrieval pipelines, feature stores, model optimization, GPU-backed training, Unity Catalog, data governance, FinOps, incident management, and production support.
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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.