Machine Learning Engineer

Key Responsibilities
Design, develop, train, test, and deploy machine learning models for enterprise-scale business applications
Analyze new and existing datasets to evaluate opportunities for model improvements and enhanced predictive performance
Build, compare, and validate multiple machine learning models to determine the most effective production solution
Perform feature engineering, model selection, hyperparameter tuning, and model optimization
Develop scalable data processing pipelines using Python and PySpark
Deploy, monitor, maintain, and improve machine learning models in production environments
Assess model performance using appropriate statistical methods and machine learning evaluation metrics
Collaborate with business stakeholders and technical teams to identify opportunities for machine learning solutions
Conduct exploratory data analysis and provide insights to support data-driven decision-making
Work with large-scale datasets within AWS cloud environments
Develop reproducible machine learning workflows and maintain technical documentation
Troubleshoot production model issues and implement continuous improvements
Support experimentation and proof-of-concept initiatives related to machine learning and AI technologies
Configure and manage local LLM environments where required to support business use cases
Participate in technical discussions, code reviews, and best practice initiatives

Required Qualifications
5+ years of experience
Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field
AWS Certified Machine Learning Specialty
AWS Certified Solutions Architect Associate or Professional
Databricks Machine Learning Certification
Google Professional Machine Learning Engineer
Relevant Python, Data Science, or Machine Learning Certifications

Skills
Python
PySpark
AWS Cloud Services
Machine Learning Model Development
Classification Algorithms
Regression
Clustering
Ensemble Methods
Random Forest
Gradient Boosting
Data Processing Pipelines
MLOps Tools and Model Monitoring Frameworks
Machine Learning Experimentation Platforms
Distributed Computing and Big Data Technologies
Cloud ML Workload Optimization
Advanced Machine Learning Algorithms and Predictive Analytics
Generative AI
Machine Learning Lifecycle Management
Model Evaluation and Validation
Feature Engineering
Hyperparameter Tuning
Statistical Modeling
Exploratory Data Analysis
Production Model Monitoring and Maintenance
Local LLM Deployment and Management
Technical Documentation
Code Review Facilitation
Stakeholder Management
Independent Project Management
Proof-of-Concept Development

Schedule
Start date: 2026-09-18