About the role
- Design, develop, train, and optimize machine learning and deep learning models using Python and relevant ML frameworks.
- Perform data preprocessing, exploratory data analysis, feature engineering, model selection, and performance evaluation.
- Apply appropriate ML algorithms and techniques to solve business problems, selecting models based on data characteristics and project requirements.
- Deploy trained models into production using suitable APIs, model-serving frameworks, or cloud-based services.
- Work with AWS services, including SageMaker and S3, to support model development, deployment, and data workflows.
- Collaborate with engineering, data, and business teams to translate requirements into effective ML solutions.
- Monitor model performance, troubleshoot issues, and contribute to model optimization and continuous improvement.
- Follow good coding practices, maintain documentation, and support scalable and maintainable ML implementations.
- 2–4 years of hands-on experience in Data Science, Machine Learning, or a related field.
- Strong proficiency in Python, with practical experience using NumPy, Pandas, and Scikit-learn.
- Good understanding of core ML concepts, including supervised and unsupervised learning, feature engineering, model evaluation, overfitting, and model validation.
- Practical experience developing and evaluating ML models; familiarity with deep learning frameworks such as TensorFlow or PyTorch is desirable.
- Hands-on experience with AWS cloud services, particularly SageMaker and S3; familiarity with Athena and Redshift is an advantage.
- Understanding of model deployment, inference, and the transition from experimentation to production.
- Strong analytical thinking, problem-solving skills, and the ability to explain technical concepts clearly.
- Experience deploying and maintaining ML models in production environments.
- Familiarity with MLOps practices, model monitoring, model drift, and performance optimization.
- Exposure to REST APIs, Docker, CI/CD, or model-serving frameworks.
- Familiarity with big data technologies such as Apache Spark.
- Exposure to time-series forecasting or other specialized ML use cases is an advantage, but not mandatory.
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or another relevant quantitative discipline.
- We are looking for someone who understands the fundamentals, can independently contribute to ML development, takes ownership of their work, and is keen to build and deploy solutions that create real business impact.
Our team is full of curious, full-stack, innovative thought leaders who are dedicated to providing outstanding customer experiences and building authentic relationships. We are compelled by our core values to drive transformational results from Ideas to Reality for clients across all company sizes, geographies, and industries. The Oneture team delivers full lifecycle solutions— from ideation,project inception, planning through deployment to ongoing support and maintenance.
Our core competencies and technical expertise includes Cloud powered: Product Engineering, Big Data and AI ML. Our deep commitment to value creation for our clients and partners and “Startups-like agility with Enterprises-like maturity” philosophy has helped us establish long-term relationships with our clients and enabled us to build and manage mission-critical platforms forthem.