Lead Machine Learning Engineer

We are seeking a talented and motivated ML Engineer to join our founding team in India. This role is central to building AI solutions on the Axyo platform that solve high-value problems for enterprise clients. As an early member of our India team, you will work closely with the VP of Data Science, Data Engineers, and Product Managers to design, train, and deploy machine learning models that power Axyo AI Apps.

Responsibilities

Applied Machine Learning & Modeling

• Design, train, and evaluate predictive, classifi cation, forecasting, and optimization models.

• Build and experiment with ML pipelines across traditional statistical methods and modern ML/deep learning approaches.

• Apply feature engineering, model selection, and hyperparameter tuning to optimize performance.

• Translate business problems into well-defi ned ML formulations and deliver measurable outcomes.

Technical Excellence & Best Practices

• Implement and document best practices for experimentation, reproducibility, and version control (MLfl ow, DVC, etc.).

• Partner with DevOps/MLOps to deploy models into production, ensuring scalability, robustness, and monitoring.

• Develop reusable model components, templates, and best practices that strengthen Axyos modular platform.

• Support client-facing proof-of-concept (POC) projects, showcasing the value of Axyo AI in solving real business challenges.

• Stay current with the latest research and technologies in ML/AI, and drive their practical application at Axyo.

• Requirements

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related technical fi eld.

• 5+ years of hands-on experience in applied machine learning or data science.

• Strong programming skills in Python with experience using ML/AI libraries and frameworks (e.g., scikit-learn, XGBoost, Statsmodels, PyTorch, TensorFlow).

• Solid understanding of statistics, probability, and machine learning algorithms (regression, classifi cation, clustering, forecasting, NLP, etc.).

• Experience with AWS cloud services for data and ML (e.g., S3, SageMaker, Lambda, SQS, CloudWatch, SNS, EMR, Airfl ow, AWS Glue).

• Familiarity with experiment design, evaluation metrics, and error analysis.

• Knowledge of MLOps practices (model versioning, monitoring, retraining).

• Strong problem-solving skills, curiosity, and ability to communicate complex results in a simple, business-friendly manner.