AWS Machine Learning Engineer

Position Summary

Seeking an experienced AWS Machine Learning Engineer with strong expertise in Machine Learning, MLOps, AWS cloud services, Databricks ML, Spark, and model operationalization. The candidate will design, build, deploy, and support scalable ML solutions in a hybrid New Jersey role.

Key Responsibilities

• Design, build, train, validate, and deploy machine learning models.
• Develop feature engineering pipelines and model training workflows.
• Implement MLOps practices including CI/CD, model monitoring, drift detection, and retraining.
• Build ML solutions using AWS SageMaker, Glue, EMR, S3, Lambda, ECS/EKS, Step Functions, and CloudWatch.
• Develop Databricks ML and Spark-based solutions using PySpark, MLflow, and Delta Lake.
• Collaborate with data scientists, architects, and business stakeholders.
• Mentor junior team members and contribute to ML strategy.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
• 6+ years of software engineering, data engineering, or machine learning experience.
• 3+ years of hands-on AWS ML engineering experience.
• Strong experience in MLOps and production ML deployments.

Technical Skills

• AWS: SageMaker, Glue, EMR, S3, Lambda, ECS/EKS, Athena, Redshift, Step Functions, CloudWatch.
• Machine Learning: Classification, Regression, NLP, Forecasting, Recommendation Systems, Feature Engineering.
• Databricks/Spark: Databricks ML, Apache Spark, PySpark, MLflow, Delta Lake, Feature Store.
• Programming: Python, SQL, PySpark.
• MLOps: Git, CI/CD, Docker, Kubernetes, Model Registry, Model Monitoring.

Preferred Qualifications

• AWS Machine Learning Specialty Certification.
• Databricks Machine Learning Certification.
• Experience with Generative AI, LLMs, RAG, and Vector Databases.
• Experience in Healthcare, Insurance, Banking, or other regulated industries.

Base Compensation Range: 70,000 to 90,000

The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits