ML Engineer || Malvern, PA - hybrid onsite || H1 with PP number mandatory

[Savi Technologies LLC]

Please share Suitable profiles along with pay rate details.

Role: ML Engineer
Location:  Malvern, PA - hybrid onsite
Duration: 6 months

Job Description

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 preferred7 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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