N- Senior AssociateCloud AIMLOps EngineerOneCloudAdvisoryPan India

Line of Service

Industry/Sector

Specialism

Management Level

Job Description & Summary

Why PWC

At PwC you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes forour clients and communities. This purpose-led and values-driven work powered by technology in an environment that drives innovation will enable you to make a tangible impact in the real world. We reward your contributions support your wellbeing and offer inclusive benefits flexibility programmes and mentorship that will help you thrive in work and life. Together we grow learn care collaborate and create a future of infinite experiences foreach other. Learn more about us.

At PwC we believe in providing equal employment opportunities without any discrimination on the grounds of gender ethnic background age disability marital status sexual orientation pregnancy gender identity or expression religion or other beliefs perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firms growth. To enable this we have zero tolerance for any discrimination and harassment based on the above considerations.

Job Description & Summary: We are seeking an experienced Cloud AI/MLOps Engineer to design build and maintain scalable AI/ML systems in cloud environments. This role combines data engineering machine learning operations and cloud infrastructure expertise to deliver production-ready AI solutions. The ideal candidate will have 4-9 years of experience in ML engineering data pipelines and cloud platforms with hands-on experience in generative AI technologies.

Responsibilities:

Design implement and maintain end-to-end ML pipelines for model training validation and deployment Develop and optimize MLOps workflows using tools like MLflow Kubeflow or SageMaker Pipelines Implement model versioning experiment tracking and automated model retraining systems Monitor model performance data drift and implement automated alerting systems Collaborate with data scientists to productionize ML models and ensure scalability Design implement and maintain end-to-end ML pipelines for model training validation and deployment Develop and optimize MLOps workflows using tools like MLflow Kubeflow or SageMaker Pipelines Implement model versioning experiment tracking and automated model retraining systems Monitor model performance data drift and implement automated alerting systems Design and implement scalable ML infrastructure on AWS using services like SageMaker EC2 EKS Lambda Manage data storage and processing using S3 RDS Redshift and EMR Implement auto-scaling solutions for ML workloads and cost optimization strategies Configure VPC security groups IAM roles and implement cloud security best practices Deploy containerized applications using AWS ECS/EKS and manage serverless architectures

Build and maintain CI/CD pipelines for ML model deployment using Jenkins GitLab CI or AWS CodePipeline Implement Infrastructure as Code (IaC) using Terraform CloudFormation or AWS CDK Containerize ML applications using Docker and orchestrate with Kubernetes Automate testing frameworks for ML models including unit tests integration tests and model validation Implement monitoring and logging solutions using CloudWatch Prometheus or ELK stack

Mandatory skill sets:

Programming Languages: Proficiency in Python with experience in SQL and bash scripting ML Frameworks: Hands-on experience with TensorFlow PyTorch Pyspark scikit-learn and Hugging Face Transformers Cloud Platforms: 3 years of AWS experience with ML-specific services (SageMaker Bedrock Comprehend) lambda ECS/EKS MLOps Tools: Experience with MLflow Kubeflow DVC or similar model management platforms DevOps Tools: Proficiency in Docker Kubernetes Terraform Jenkins Git and CI/CD practices Data Engineering Fundamentals: ETL/ELT Pipelines Data Warehousing Concepts Apache Spark (Basic) Data Quality & Validation Basic Generative AI: LLM APIs (OpenAI/AWS Bedrock) Basic Prompt Engineering Vector Databases.

Preferred skill sets:

Programming & Languages: Java/Scala Go/Rust JavaScript/TypeScript R Advanced ML/AI: Hugging Face Transformers XGBoost/LightGBM Apache Spark MLlib ONNX TensorFlow Extended (TFX) Multi-Cloud & Advanced Infrastructure: Azure ML/Google Cloud AI Edge Computing Advanced Serverless Cloud Cost Optimization

MLOps & Orchestration Tools: MLflow Kubeflow DVC Weights & Biases Apache Airflow DevOps & Infrastructure: Helm Charts Istio/Service Mesh Prometheus/Grafana ELK Stack HashiCorp Vault AI/ML Specializations: Computer Vision (OpenCV) NLP Time Series Analysis Reinforcement Learning AutoML Advanced Generative AI: LLM Fine-tuning RAG Systems LangChain/LlamaIndex Vector Search Optimization Multi-modal AI Data Engineering: Apache Kafka Apache Spark (Advanced) Databricks Snowflake dbt AWS EMR/Glue Stream Processing Data Mesh Architecture Security & Compliance: ML Security Data Privacy (GDPR/HIPAA) Model Governance Federated Learning

Years of experience required:

4-9 Years

Education qualification:

B. E/ B. Tech

Education (if blank degree and/or field of study not specified)

Certifications (if blank certifications not specified)

Required Skills

Optional Skills

Desired Languages (If blank desired languages not specified)

Travel Requirements

Available for Work Visa Sponsorship

Government Clearance Required

Job Posting End Date