Senior Python Engineer Machine Learning Systems

Job Role: Senior Python Engineer Machine Learning Systems

Job Type: Full-Time

Work Term: W2 Only

Experience: 8+ Years

Interviews: 2 Rounds Virtual

Responsibilities:

Design, develop, and deploy scalable machine learning and deep learning models to solve complex business problems.

Build data pipelines and APIs for model training, evaluation, and inference using Python.

Work collaboratively with data scientists, engineers, and product teams to integrate ML solutions into production systems.

Lead the implementation of ML model lifecycle best practices including data preprocessing, model versioning, and monitoring.

Optimize Python code for performance, scalability, and reliability in distributed computing environments.

Mentor junior developers and contribute to code reviews, architectural discussions, and design decisions.

Communicate complex ML concepts and results clearly to technical and non-technical stakeholders.

Stay up to date with the latest advancements in machine learning frameworks, Python libraries, and cloud services.

Job Duties:

Develop Python-based services and libraries supporting machine learning workflows.

Build and manage training pipelines using frameworks like TensorFlow, PyTorch, or Scikit-learn.

Write reusable, production-ready code for ML models, utilities, and APIs (Flask/FastAPI).

Use cloud platforms (AWS/Google Cloud Platform/Azure) to scale model training and deployment using containers or serverless compute.

Implement CI/CD pipelines for automated model validation, testing, and release.

Monitor production ML systems for performance drift and automate retraining triggers.

Collaborate on data cleaning, feature extraction, transformation, and exploratory data analysis (EDA).

Work with large datasets using tools like Pandas, NumPy, Spark, or Dask.

Perform model performance evaluation and statistical validation (A/B testing, cross-validation, ROC/AUC).

Develop dashboards or reports to visualize model metrics and outcomes.

Required Skill Sets:

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.

8+ years of Python development experience in production-grade environments.

4+ years working on machine learning or AI-based systems.

Proficiency with Python libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.

Solid experience building RESTful APIs using Flask, FastAPI, or Django REST Framework.

Experience deploying ML models in cloud platforms (AWS SageMaker, Google Cloud Platform AI Platform, or Azure ML).

Strong understanding of software engineering practices, data structures, and algorithm optimization.

Experience with version control (Git), CI/CD pipelines, Docker, and containerized deployments.

Hands-on with relational and NoSQL databases (PostgreSQL, MongoDB, etc.).

Strong knowledge of data preprocessing, model evaluation, and statistical methods.

Desired Skill Sets:

Familiarity with data engineering tools like Apache Spark, Airflow, Kafka, or Dask.

Experience in MLOps and using tools like MLflow, Kubeflow, or SageMaker Pipelines.

Exposure to NLP, Computer Vision, or Reinforcement Learning models.

Experience in developing automated test frameworks for ML pipelines.

Strong verbal and written communication skills with ability to explain technical content to diverse audiences.

Open-source contributions or publications in ML conferences/journals is a plus.