Design and implement end-to-end Machine Learning pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring 

Deploy and manage Machine Learning models in production across AWS, Azure, and Snowflake-based ecosystems 

Build batch and real-time inference pipelines using cloud-native and platform-native services 

Automate model packaging, testing, release, and rollback using CI/CD best practices 

Integrate Machine Learning workflows with services such as AWS SageMaker, AWS Lambda, Azure Machine Learning, Azure Data Factory, and Snowflake 

Build and maintain orchestration workflows using tools such as Airflow, Azure Data Factory, or similar platforms 

Implement experiment tracking, model registry, and model governance processes 

Monitor model accuracy, drift, latency, throughput, pipeline failures, and infrastructure usage 

Establish deployment strategies such as canary, shadow, blue-green, and rollback mechanisms 

Collaborate with cross-functional teams to move models from research to production 

Ensure security, compliance, traceability, and access control for models and data across cloud environments 

Optimize platform performance, reliability, and cost across AWS, Azure, and Snowflake 

Document architecture, deployment standards, and operational procedures 

Master’s or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar 

Five or more years of relevant experiences 

Proven experience in MLOps, Machine Learning engineering, platform engineering, or DevOps 

Strong hands-on experience with AWS, MS Azure, and Snowflake 

Strong programming skills in Python and SQL 

Experience deploying and managing Machine Learning models in production 

Experience with cloud Machine Learning services such as AWS SageMaker and Azure Machine Learning 

Experience building data pipelines and integrating with Snowflake 

Knowledge of CI/CD pipelines, infrastructure automation, and model versioning 

Experience with containerization and orchestration tools such as Docker and Kubernetes 

Experience with workflow orchestration tools such as Airflow, Azure Data Factory, or similar 

Familiarity with model monitoring, logging, alerting, and observability 

Solid understanding of data engineering concepts, APIs, and distributed processing 

Strong troubleshooting, communication, and cross-team collaboration skills 

Experience with Snowflake Cortex AI, Snowpark, or Machine Learning workloads in Snowflake 

Experience with AWS Bedrock, Azure OpenAI, or production LLM workflows 

Experience with real-time inference, event-driven pipelines, and serverless architectures 

Familiarity with feature stores, vector databases, and RAG-based systems 

Experience with Terraform, CloudFormation, or Azure infrastructure-as-code tools 

Understanding of security, compliance, and governance requirements for regulated environments 

Experience with production A/B testing, shadow deployment, and rollback strategies