N- Senior Associate_Cloud AI/MLOps Engineer_OneCloud_Advisory_Pan 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 for our 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 for each 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 firm’s 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