DevOps Engineer

🚀 We’re Hiring at PwC India! 🚀

#PwC_IN_DA_2026

We’re expanding our Data & Analytics team and looking for talented professionals across India! 🌐

If you’re passionate about working with cutting-edge technologies and want to build your career with a global consulting leader, this is your chance.

Education: B. Tech, M. Tech, B.E, MBA

Interested candidates please fill below form:

https://lnkd.in/d-rUtpYj

Please Send your resume to [email protected]

Please mention subject line as Job Application- Skillset

🔎 Open Roles & Skill Areas

Azure devops (Pan India, 4 to 10 years experience)

Deep knowledge of Azure Machine Learning Studio, Azure Databricks, and Azure OpenAI Service.

Programming: Strong Python programming skills for data manipulation and model creation.DevOps

Tools: Expertise in Azure DevOps/GitHub Actions for automation.

Frameworks: Familiarity with MLflow for experiment tracking and model packaging.

Containerization: Proficiency with Docker and Kubernetes (AKS) for container orchestration.

AWS DevOps (Pan India, 4 to 10 years experience)

Pipeline Automation: Build and maintain CI/CD pipelines for machine learning workflows to enable rapid, automated deployment.

Production Deployment: Operationalize ML models developed by scientists, moving them from prototypes to scalable, real-time production APIs (using Amazon SageMaker, EKS, Lambda).

Monitoring & Governance: Implement monitoring systems to track model performance, data drift, and latency.

Infrastructure Management: Provision and manage AWS infrastructure (S3, EC2, CloudFormation) tailored for ML training and inference.

Collaboration: Work with data scientists to optimize models for production and with DevOps for infrastructure.GenAI/LLM Focus: In cutting-edge roles, establish robust pipelines for LLM fine-tuning and deployment (using tools like LangChain or Amazon Bedrock).

GCP Devops (Pan India, 4 to 10 years experience)

Pipeline Automation & Orchestration: Design and implement CI/CD pipelines for continuous training, evaluation, and deployment of ML models using Vertex AI, Airflow, or Kubeflow.

Infrastructure Management: Build and maintain scalable, secure infrastructure on GCP (Vertex AI, Cloud Functions, GKE, BigQuery).

Model Monitoring & Observability: Implement monitoring to detect model drift, data quality issues, and performance degradation to ensure reliability.

Data Pipeline Engineering: Develop and maintain data pipelines for collecting, cleaning, and transforming data from diverse sources.Experiment Tracking & Versioning: Implement version control for code, data, and models to ensure reproducibility (e.g., using Vertex AI Experiments).