Pyramid Consulting, Inc. is seeking a talented Data Engineer – ML Platform for a contract opportunity with long-term potential. The role involves building and operating data platforms in AWS, contributing to ML Platform or MLOps frameworks, and ensuring best practices in model lifecycle management and deployment.
Responsibilities:
- Must have skills: - ML Platform, MLOps pipelines, AWS, Python, SQL
- 7+ years of experience building and operating data platforms in AWS
- Demonstrated experience designing or contributing to a shared ML Platform or MLOps framework used by multiple teams
- Expertise in Python and SQL, with a strong track record of production-grade data and ML systems
- Strong hands-on experience with MLOps best practices, including:
- Model lifecycle management and deployment patterns
- Feature engineering and reusable feature pipelines
- Experiment tracking, reproducibility, and model governance
- Model performance monitoring and drift detection
- Advanced knowledge of AWS services including Glue, Lambda, ECS Fargate, and Apache Spark, with experience operating them at scale
- Strong experience delivering containerized, infrastructure-as-code solutions using Docker and CDK
- Deep understanding of data warehousing, lakehouse, and ML-ready data architectures, including Delta Lake; Snowflake experience is a strong plus
- Experience with Arrow-based data and streaming technologies (ADBC, Arrow ODBC, PyArrow) a plus
- Experience with Airflow or Dagster (or similar orchestration platforms) for managing complex data and ML workflows a plus
- Proven ability to influence standards, architecture, and best practices across engineering and data science teams
- Excellent communication skills, with the ability to translate complex platform and ML concepts for technical and non-technical stakeholders
Requirements:
- Must have skills: - ML Platform, MLOps pipelines, AWS, Python, SQL
- 7+ years of experience building and operating data platforms in AWS
- Demonstrated experience designing or contributing to a shared ML Platform or MLOps framework used by multiple teams
- Expertise in Python and SQL, with a strong track record of production-grade data and ML systems
- Strong hands-on experience with MLOps best practices, including: Model lifecycle management and deployment patterns, Feature engineering and reusable feature pipelines, Experiment tracking, reproducibility, and model governance, Model performance monitoring and drift detection
- Advanced knowledge of AWS services including Glue, Lambda, ECS Fargate, and Apache Spark, with experience operating them at scale
- Strong experience delivering containerized, infrastructure-as-code solutions using Docker and CDK
- Deep understanding of data warehousing, lakehouse, and ML-ready data architectures, including Delta Lake; Snowflake experience is a strong plus
- Proven ability to influence standards, architecture, and best practices across engineering and data science teams
- Excellent communication skills, with the ability to translate complex platform and ML concepts for technical and non-technical stakeholders
- Experience with Arrow-based data and streaming technologies (ADBC, Arrow ODBC, PyArrow) a plus
- Experience with Airflow or Dagster (or similar orchestration platforms) for managing complex data and ML workflows a plus