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Skills

Amazon S3
Amazon SageMaker
Continuous Delivery
Continuous Integration
Cloud Computing
Collaboration
Conflict Resolution
Apache Airflow
Apache Spark
Artificial Intelligence
Amazon Web Services
Analytical Skill
Machine Learning (ML)
Large Language Models (LLMs)
Lifecycle Management
Generative Artificial Intelligence (AI)
Kubernetes
Docker
Emerging Technologies
Evaluation
Innovation
Microsoft Azure
Orchestration
Performance Tuning
Machine Learning Operations (ML Ops)
Management
Data Processing
Databricks
DevOps
Scalability
Optimization
Problem Solving
PySpark
Python
Analytics
Data Architecture
Data Governance
Use Cases
Data Science
SQL
Step-Functions
Training
Unity
Workflow
AWS
ML
Machine Learning
AI

Amazon S3
Amazon SageMaker
Continuous Delivery
Continuous Integration
Cloud Computing
Collaboration
Conflict Resolution
Apache Airflow
Apache Spark
Artificial Intelligence
Amazon Web Services
Analytical Skill
Machine Learning (ML)
Large Language Models (LLMs)
Lifecycle Management
Generative Artificial Intelligence (AI)
Kubernetes
Docker
Emerging Technologies
Evaluation
Innovation
Microsoft Azure
Orchestration
Performance Tuning
Machine Learning Operations (ML Ops)
Management
Data Processing
Databricks
DevOps
Scalability
Optimization
Problem Solving
PySpark
Python
Analytics
Data Architecture
Data Governance
Use Cases
Data Science
SQL
Step-Functions
Training
Unity
Workflow
AWS
ML
Machine Learning
AI

Summary

Role: Machine Learning Engineer – Databricks

Location: Malvern, PA (Hybrid – 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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90999382
Position Id: 9039256
Posted 5 hours ago

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Anchal Khapekar

Recruiter @ Aptino

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Machine Learning Engineer – Databricks

Skills

Summary

Role: Machine Learning Engineer – Databricks

Position Overview:

Key Responsibilities:

Required Qualifications:

Preferred Qualifications:

Dice Job Match Score™

Anchal Khapekar

Similar Jobs

Machine Learning Engineer – Databricks

Job Details

Skills

Summary

Role: Machine Learning Engineer – Databricks

Position Overview:

Key Responsibilities:

Required Qualifications:

Preferred Qualifications:

Dice Job Match Score™

Anchal Khapekar

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