Machine Learning Engineer
About Talemexa Talemexa Private Limited delivers recruitment, staffing, and workforce solutions that help organizations source, engage, and retain skilled professionals. The company focuses on recruitment consulting, talent acquisition, and workforce support to help businesses build capable teams and adapt to evolving talent requirements. Through technology-driven approaches and collaborative solutions, Talemexa aims to support efficient operations and better business outcomes.
About the Role:
Talemexa is seeking a skilled and results-driven Machine Learning Engineer to design, develop, deploy, and maintain production-grade machine learning systems. The ideal candidate will have strong programming skills, practical experience with machine learning frameworks, and an understanding of the complete machine learning lifecycle. In this role, you will work on predictive modeling, personalization, ranking, and recommendation systems. You will collaborate with data scientists, infrastructure engineers, and product teams to transform machine learning concepts into reliable, scalable applications. From data preparation and feature engineering to deployment, monitoring, and optimization, you will help ensure that machine learning solutions deliver measurable value in real-world environments.
Key Responsibilities:
Design, develop, train, and implement machine learning models for prediction, personalization, ranking, and recommendation.
Build reliable data preparation pipelines to process, clean, transform, and validate datasets.
Perform feature engineering to improve model performance and predictive accuracy.
Train, evaluate, and optimize machine learning models using appropriate algorithms, metrics, and frameworks.
Use Python, SQL, and machine learning libraries such as PyTorch, TensorFlow, or Scikit-learn to develop practical solutions.
Deploy trained models into production environments and integrate them with existing applications and services.
Develop automated workflows for model monitoring, performance evaluation, and retraining.
Track model quality and identify issues such as data drift, performance degradation, and inconsistent predictions.
Optimize model inference speed, computational efficiency, scalability, and resource utilization.
Work with cloud infrastructure and MLOps tools to streamline model deployment and lifecycle management.
Use containerization technologies such as Docker to support consistent development and deployment environments.
Collaborate with data scientists, infrastructure engineers, and product teams to align technical solutions with business requirements.
Maintain technical documentation covering model architecture, training processes, deployment procedures, and performance metrics.
Follow responsible AI practices and promote reliability, transparency, and appropriate data usage.
Qualifications and Experience:
- Bachelor's or master's degree in Computer Science, Machine Learning, Mathematics, Data Science, or a related field, or equivalent practical experience.3–6 years of professional experience in machine learning engineering or a related technical discipline.
- Strong proficiency in Python and practical experience using SQL.Hands-on experience with PyTorch, TensorFlow, Scikit-learn, or comparable machine learning frameworks.
- Solid understanding of statistical modeling, machine learning algorithms, model evaluation, and optimization techniques.
- Experience deploying and maintaining machine learning models in production environments.
- Familiarity with cloud computing platforms, containerization, and MLOps workflows.
- Understanding of data pipelines, feature engineering, model monitoring, and model retraining.
- Strong analytical thinking, debugging, and problem-solving abilities.
- Effective communication skills and the ability to collaborate across technical and product teams.
- Required Skills and Competencies
- Python Programming
- Machine Learning and Predictive Modeling
- PyTorch and TensorFlowScikit-learnSQL and Data Processing
- Feature Engineering and Data Pipelines
- Model Evaluation and OptimizationMLOps and Model Lifecycle Management
- Production Model Deployment
- Docker and Containerization
- Cloud Computing and Infrastructure
- Model Monitoring and Retraining
- Performance Optimization and Scalability
- Responsible AI Practices
- Technical Documentation and Team Collaboration
- Why Join Talemexa?
Join Talemexa to contribute to practical machine learning initiatives and develop systems designed for real-world use. This role offers the opportunity to work across the machine learning lifecycle, collaborate with multidisciplinary teams, and strengthen your expertise in model deployment, MLOps, cloud infrastructure, and scalable AI solutions.
Equal Opportunity Employer:
Talemexa Private Limited is committed to fair and inclusive recruitment. We welcome qualified applicants from diverse backgrounds and aim to provide a professional environment that supports collaboration, continuous learning, and career development.