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Weekend Machine Learning Jobs in Huntingdon Valley, PA

Senior Machine Learning Engineer

Malvern, PA

$120K - $158K/yr

We are assisting our client in hiring for a Senior Machine Learning Engineer. Our client is an established SaaS company serving banks, credit unions, and fintechs. Their cloud-based platform helps ...

New

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Machine Learning Engineer 3- 7882

Philadelphia, PA ยท On-site +1

$56.25 - $74.50/hr

... machine learning techniques including tree-based models, linear and logistic regression, and time series models; use scikitlearn to create models; perform statistical modeling using techniques ...

Senior Data & Machine Learning Engineer

Malvern, PA ยท On-site

$112K - $134K/yr

THE OPPORTUNITY AKUVO is seeking a hands-on Senior Data & Machine Learning Engineer to build and own the production lifecycle of our proprietary predictive models and scores. This is a depth role ...

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Weekend Machine Learning information

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Machine Learning Engineer - Databricks

Aptino

Malvern, PA โ€ข On-site

Other

Posted 2 days ago

New


Job description

Role: Machine Learning Engineer โ€“ Databricks

Location: Malvern, PA (Hybrid โ€“ 3 days/week onsite)
Duration: 12 Months

Position Overview:

We are seeking an experienced Machine Learning Engineer with strong expertise in the Databricks Lakehouse Platform to develop, deploy, and optimize scalable AI/ML solutions. The ideal candidate will have hands-on experience building production-ready machine learning models, implementing MLOps best practices, and designing end-to-end ML pipelines using Databricks, Spark, and AWS cloud services.

Key Responsibilities:
  • Design, develop, and deploy scalable machine learning models using the Databricks Machine Learning platform.
  • Build and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
  • Utilize MLflow for experiment tracking, model versioning, lifecycle management, and production deployments.
  • Develop high-performance data processing pipelines using PySpark, Apache Spark, and SQL to support large-scale analytics and machine learning workloads.
  • Build and maintain production-grade applications and ML workflows on AWS, leveraging services such as Lambda, S3, Glue, ECS/EKS, Step Functions, SageMaker, and Bedrock.
  • Implement feature engineering, model evaluation, hyperparameter optimization, and performance tuning to improve model accuracy and scalability.
  • Collaborate with data engineers, data scientists, and business stakeholders to translate business requirements into production-ready ML solutions.
  • Establish MLOps best practices, CI/CD processes, monitoring strategies, and governance standards for machine learning deployments.
  • Optimize data architecture and machine learning workflows using Databricks Lakehouse, Delta Lake, and Unity Catalog.
  • Contribute to AI innovation initiatives by evaluating emerging technologies, Generative AI use cases, and modern machine learning frameworks.
Required Qualifications:
  • 5+ years of experience in Machine Learning, Artificial Intelligence, or Data Science engineering.
  • Strong hands-on expertise with Databricks Machine Learning and the Databricks ecosystem.
  • Proven experience using MLflow for experiment management, model registry, and deployment.
  • Strong programming skills in Python, PySpark, Apache Spark, and SQL.
  • Experience developing supervised and unsupervised machine learning models for enterprise applications.
  • Hands-on experience building, deploying, and supporting production-grade ML pipelines.
  • Solid understanding of feature engineering, model validation, hyperparameter tuning, and model performance optimization.
  • Experience developing cloud-native AI/ML solutions on AWS.
  • Experience building scalable data pipelines using Spark, Glue, Airflow, dbt, or similar orchestration tools.
  • Strong analytical, troubleshooting, and problem-solving skills with experience handling large datasets.
Preferred Qualifications:
  • Experience designing solutions on the Databricks Lakehouse Architecture.
  • Knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI-powered applications.
  • Experience with orchestration frameworks such as Apache Airflow or Azure Data Factory.
  • Familiarity with Docker, Kubernetes, CI/CD, and modern DevOps practices.
  • Hands-on experience with Delta Lake, Unity Catalog, and enterprise data governance.
  • Databricks certification is an added advantage.