Machine Learning Engineer - Python

Location : Remote / India

Company : PranaTree LLC

About Prana Tree :

Prana Tree is a fast-growing IT consulting and product innovation firm specializing in AI, Data Engineering, Blockchain, and Cloud solutions. We partner with global enterprises and start-ups to deliver transformative technologies that solve real-world problems. At Prana Tree, we believe in nurturing talent, driving innovation, and building a future rooted in ethical AI and intelligent automation. Our collaborative, high-impact culture fosters learning, ownership, and end-to-end product thinking.

Role Overview :

We are looking for a Machine Learning Engineer who has hands-on experience building predictive models using traditional ML algorithms. The ideal candidate will be responsible for designing, developing, and deploying end-to-end ML pipelines to support business decision-making and forecasting. This is a highly technical and impactful role that requires both depth in modeling and breadth in data handling and engineering.

Key Responsibilities :

- Design and implement robust, production-grade end-to-end ML pipelines for predictive analytics.

- Develop and optimize models using traditional ML techniques such as XGBoost, Gradient Boosting, Random Forest, and similar supervised learning algorithms.

- Conduct feature engineering, data cleaning, and data transformation for large-scale datasets.

- Collaborate with data engineering and product teams to define and apply business rules and ensure model relevance and accuracy.

- Evaluate model performance using relevant metrics, and iterate on improvements.

- (Optional) Apply probabilistic forecasting techniques like MCMC modeling where relevant, especially in time series or uncertainty-driven problems.

- Write clean, modular, and production-ready Python code using best practices and version control systems.

- Document processes, results, and learnings to ensure knowledge transfer and reproducibility.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.

- 2 to 5 years of industry experience in building ML solutions using supervised learning methods.

- Strong hands-on expertise in Python and popular libraries such as pandas, scikit-learn, xgboost, lightgbm, etc.

- Experience building scalable ML pipelines from data ingestion to model deployment.

- Solid understanding of model evaluation, validation strategies, and generalization techniques.

- Excellent problem-solving skills and ability to translate business needs into data-driven models.

Good to Have or Optional Skills :

- Exposure to probabilistic modeling frameworks such as PyMC3, Stan, or NumPyro.

- Experience with time series forecasting or Bayesian methods.

- Familiarity with Docker, MLflow, or pipeline orchestration tools.

- Knowledge of cloud platforms such as AWS or GCP for ML deployment.