Machine Learning Engineer

Compunnel, Inc.; Philadelphia (Philadelphia County); On-site; USD 140,000 - 200,000; Full time

Job summary

Compunnel, Inc. is seeking a highly experienced Senior Machine Learning Engineer to join a team building production-grade predictive systems for large-scale enterprise applications.

This role focuses on end-to-end ML engineering with Python, PySpark, and big data platforms such as Databricks or Snowflake. The ideal candidate will design, develop, deploy, and maintain production ML models, monitor performance, and collaborate with data engineering and software teams to ensure reliability and

Qualifications

• Built machine learning models from scratch.
• Experience with Python and PySpark.
• Experience deploying models to production.
• Strong data preprocessing and feature engineering.
• Experience with large-scale data platforms (Databricks/Snowflake).
• Mentoring junior engineers and collaborating with teams.
• Knowledge of model evaluation metrics.

Responsibilities

• Design, build, train, validate, and deploy production ML models.
• Develop predictive models using various algorithms.
• Build scalable PySpark pipelines for large datasets.
• Write clean, production-grade Python code and APIs.
• Monitor models and manage retraining and drift.
• Lead technical reviews and mentor junior engineers.

Skills

Python development

Machine Learning engineering

Production monitoring

Mentoring

Big data analytics

Education

Tools

Databricks

Snowflake

Delta Lake

REST APIs

Job description

JOB SUMMARY

We are seeking a highly experienced Senior Machine Learning Engineer to join a team building production-grade predictive systems for large-scale enterprise applications. This is a hands‑on engineering role focused on designing, developing, deploying, and maintaining machine learning models that solve complex business problems using large-scale structured data. This is not a traditional AI Engineer or Prompt Engineering role. Candidates whose experience is primarily centered around LLMs, ChatGPT integrations, prompt engineering, or AI agents without a strong foundation in Machine Learning Engineering will not be considered. The ideal candidate is an experienced Machine Learning Engineer who has built, trained, deployed, monitored, and maintained production ML models while working extensively with Python, PySpark, and large-scale data platforms such as Databricks or Snowflake. Experience with Generative AI, RAG, or AI agents is considered a plus but is secondary to strong production ML expertise.

Key Responsibilities

Machine Learning Engineering

• Design, build, train, validate, and deploy production Machine Learning models.
• Develop predictive models using algorithms such as Random Forest, XGBoost, CatBoost, Gradient Boosting Ensemble Models, Regression and Classification algorithms.
• Perform feature engineering, feature selection, data preprocessing, and model optimization.
• Conduct hyperparameter tuning and model evaluation using appropriate statistical metrics.
• Deploy production‑ready inference pipelines.
• Monitor production models for drift, performance degradation, and retraining requirements.
• Improve model accuracy, scalability, and operational reliability.

Big Data Engineering

• Build scalable PySpark pipelines for ingesting, cleaning, transforming, and preparing large enterprise datasets.
• Work with distributed data processing frameworks and enterprise data warehouses.
• Develop efficient ETL/ELT pipelines supporting production ML workflows.
• Optimize Spark jobs for performance and scalability.
• Work with technologies such as Databricks, Snowflake, Delta Lake, or similar big data platforms.

Software Engineering

• Write clean, maintainable, production-quality Python code.
• Build scalable APIs and backend services supporting ML inference.
• Participate in code reviews and engineering best practices.
• Build automated testing, deployment, and monitoring pipelines.
• Troubleshoot production issues and optimize system performance.

Production ML Operations

• Deploy ML models into production environments.
• Implement monitoring, alerting, and automated retraining strategies.
• Track model performance using appropriate business and technical metrics.
• Collaborate with Data Engineering and Software Engineering teams to operationalize ML solutions.

Technical Leadership

• Mentor junior Machine Learning Engineers.
• Review model implementations and engineering designs.
• Provide technical guidance on model development and production best practices.
• Assist in troubleshooting model performance and production issues.

Required Qualifications

• Built machine learning models from scratch.
• Selected appropriate algorithms based on business problems.
• Performed feature engineering and data preparation.
• Evaluated model performance using appropriate metrics.
• Deployed models into production.
• Monitored model performance and handled retraining.
• Worked extensively with large datasets using PySpark.
• Written production-quality Python code.

Ideal Candidate Profile

• Deep hands‑on Machine Learning Engineering experience.
• Strong Python development skills.
• Extensive PySpark and big data experience.
• Production deployment of multiple ML models.
• Excellent understanding of model evaluation and production monitoring.
• Ability to mentor junior engineers while remaining hands‑on.
• Strong communication skills with the ability to clearly explain technical decisions and real‑world project experience.