Location: Malvern, PA - hybrid onsite
Duration: 6 months ML Engineer
Must have Skills:
ML models AWS SageMaker AWS services
Responsibilities -
Design develop and optimize complex data pipelines using machine learning engineering best practices to ensure scalability efficiency and reliability -
Develop and implement robust MLOPS pipelines to support the deployment monitoring and lifecycle management of AI or ML models in production environments. -
Integrate and maintain data and model pipelines proactively diagnosing data quality issues and documenting assumptions. -
Collaborate closely with data scientists to validate model ready datasets and ensure thorough accurate feature documentation.
Conduct exploratory data analysis and discovery on raw data sources incorporating business context to support model development.
Track data lineage and perform root cause analysis during early-stage of exploration or issue resolution.
Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
Develop and maintain model monitoring scripts, investigate alerts and coordinate timely resolution. -
Bachelors degree in a relevant field required masters degree preferred
7 plus years of relevant experience in AI engineering or machine learning engineering or data engineering experience
Minimum 3 plus years of hands-on experience building ETL pipelines using AWS services
Proven experience developing and implementing MLs pipeline for deploying monitoring and managing AI or ML models in production.
Proficient in Python And familiar with key machine learning frameworks and libraries - Strong understanding of cloud technologies and AI or ML platforms like AWS SageMaker.
Solid grasp of software engineering principles including design patterns testing security and version control.
Knowledge of the machine learning development life cycle (MDLC) and AI engineering best practices - Experience designing and implementing end to end machine learning pipelines and solution architectures
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