Principal Scientist, Data

JOB DESCRIPTION

Must-have:Hands-on experience with AWS, Microsoft Azure, and Snowflake in building or supporting production ML/data platforms.

Job Summary

We are seeking an MLOps Engineer to design, deploy, monitor, and maintain machine learning solutions in production across AWS, Microsoft Azure, and Snowflake environments. This role will partner with data scientists and cloud teams to operationalize ML models, automate pipelines, and build reliable, secure, and scalable ML platforms.

The ideal candidate has strong experience in the end-to-end ML lifecycle, cloud-native deployment, CI/CD automation, model monitoring, and production data pipelines, with hands-on expertise in AWS, Azure, and Snowflake.

Key Responsibilities

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

Deploy and manage ML 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 ML 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

Required Qualifications

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

Five or more years of relevant experiences

Proven experience in MLOps, ML engineering, platform engineering, or DevOps

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

Strong programming skills in Python and SQL

Experience deploying and managing ML models in production

Experience with cloud ML 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

Preferred Qualifications

Experience with Snowflake Cortex AI, Snowpark, or ML 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