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/GCP/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:

Bachelors or Masters 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 GCP 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.