Machine Learning Engineer | Data Analytics & Infrastructure Expert

Summary

Machine Learning Engineer with strong foundation in large-scale infrastructure systems, currently pursuing Master's degree in Data Analytics and Modeling with focus on ML model training, optimization, and deployment. Proven track record of developing data pipelines, implementing automated solutions, and conducting statistical analysis across enterprise environments. Experienced in Python programming, deep learning frameworks, and experimental design with hands-on projects in neural networks, ensemble learning, and statistical modeling. Seeking to leverage technical expertise and academic ML training to advance large language model training and optimization.

Expectations

"I'm looking for three things:
First, challenging technical problems in machine learningespecially around model training, optimization, and deploying ML systems at scale. I want to work on projects where the technical complexity matches my skills and pushes me to grow.
Second, the opportunity to bridge ML theory and production engineering. My background gives me both perspectives, and I'm looking for a role where that's valuablewhere I can help ensure that ML models don't just work in notebooks, but perform reliably in production systems.
Third, a team and company culture focused on continuous learning. The ML field evolves rapidly, and I want to be somewhere that encourages staying current, experimenting with new approaches, and investing in professional development.
If I can find these three things, I know I'll be able to make strong contributions while continuing to develop my expertise."

Employment Preferences

Expected Base Salary

**5,000 USD

Expected Total Compensation

**5,000 USD

Expected Hourly Rate

** USD/hr

Academic Degree

Experience

Skills

• Programming
• ML Python
• R
• PowerShell
• PyTorch
• TensorFlow
• Scikit-learn
• Machine Learning Neural Networks
• Deep Learning
• Ensemble Methods
• Clustering
• Statistical Modeling
• Bayesian Learning
• Optimization
• Data Engineering Data Pipeline Development
• ETL Processes
• Data Preprocessing
• Feature Engineering
• Data Augmentation
• Development Model Evaluation
• Hyperparameter Tuning
• Experiment Design
• Statistical Analysis
• Azure
• AWS
• Git
• CI
• CD
• Infrastructure Linux
• Windows
• Distributed Systems
• VMware
• Containerization
• Enterprise Architecture
• Databricks
• ServiceNow