Job ID: JOB_ID_8177
About the Role:
Join a dynamic team focused on deploying and managing advanced machine learning solutions across cloud-native environments. This role involves designing scalable ML pipelines, automating deployment processes, and ensuring robust model monitoring within AWS, Microsoft Azure, and Snowflake ecosystems. Ideal for a senior data scientist or ML engineer with deep expertise in cloud-based ML operations and platform engineering.
Key Responsibilities:
- Design and implement comprehensive end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and ongoing monitoring.
- Deploy and manage machine learning models in production environments across AWS, Azure, and Snowflake-based platforms.
- Build batch and real-time inference pipelines utilizing cloud-native and platform-native services.
- Automate model packaging, testing, release, and rollback processes following CI/CD best practices.
- Integrate ML workflows with cloud services such as AWS SageMaker, AWS Lambda, Azure Machine Learning, Azure Data Factory, and Snowflake.
- Develop and maintain orchestration workflows using tools like Airflow, Azure Data Factory, or similar platforms.
- Implement experiment tracking, model registry, and governance processes to ensure compliance and traceability.
- Monitor model performance metrics including accuracy, drift, latency, throughput, and pipeline health.
- Establish deployment strategies such as canary, shadow, blue-green, and rollback mechanisms to ensure reliable releases.
- Collaborate with cross-functional teams to transition models from research to production environments.
- Ensure security, compliance, and access control for models and data across multiple cloud environments.
- Optimize platform performance, reliability, and cost-efficiency across AWS, Azure, and Snowflake.
- Document architecture, deployment standards, and operational procedures to support ongoing maintenance and scalability.
Required Qualifications:
- Masters or advanced degree (PhD) in Computer Science, Computer Engineering, or a related field.
- Five or more years of relevant experience in ML engineering, MLOps, or platform engineering.
- Proven hands-on experience deploying and managing ML models in production environments.
- Strong expertise with AWS, Microsoft Azure, and Snowflake platforms.
- Proficiency in Python and SQL programming.
- Experience with cloud ML services such as AWS SageMaker and Azure Machine Learning.
- Demonstrated ability to build and maintain data pipelines and integrate with Snowflake.
- Knowledge of CI/CD pipelines, infrastructure automation, and model versioning.
- Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
- Familiarity with workflow orchestration tools such as Airflow or Azure Data Factory.
- Experience with model monitoring, logging, alerting, and observability practices.
- Strong troubleshooting, communication, and cross-team collaboration skills.
Preferred Qualifications:
- Experience with Snowflake Cortex AI, Snowpark, or ML workloads in Snowflake.
- Familiarity with AWS Bedrock, Azure OpenAI, or production LLM workflows.
- Experience with real-time inference, event-driven pipelines, and serverless architectures.
- Knowledge of feature stores, vector databases, and RAG-based systems.
- Proficiency with infrastructure-as-code tools such as Terraform or CloudFormation.
- Understanding of security, compliance, and governance requirements for regulated environments.
- Experience with production A/B testing, shadow deployment, and rollback strategies.
Special Requirements
Visa constraints: None specified. Screening steps: Not specified. Interview modes: Not specified. Domain restrictions: Not specified.
Compensation & Location
Salary: $100 – $150 per year (Estimated)
Location: Houston, TX
Recruiter / Company – Contact Information
Email: waalshailja@rulesiq.com
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