We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning data science and software engineering with a passion for building and deploying scalable machine learning solutions. You will work closely with crossfunctional teams to design develop and implement cuttingedge AI and ML models that solve realworld problems.
Key Responsibilities
- Design develop and deploy machine learning models and algorithms to solve business challenges.
- Build and optimize data pipelines for training testing and deploying ML models.
- Perform feature engineering and data preprocessing to improve model performance.
- Collaborate with data scientists software engineers and product teams to understand requirements and deliver solutions.
- Evaluate the performance of ML models and implement enhancements to improve accuracy and scalability.
- Integrate ML models into production systems ensuring robustness and reliability.
- Stay updated with the latest advancements in AI/ML technologies and propose innovative solutions.
- Conduct A/B testing experiment tracking and model monitoring in production environments.
Requirements
- Bachelor s/Master s degree in Computer Science Data Science Machine Learning or a related field.
- Proficiency in Python and ML libraries/frameworks such as TensorFlow PyTorch Scikitlearn etc.
- Strong understanding of machine learning algorithms (e.g. supervised unsupervised reinforcement learning).
- Experience with data preprocessing feature engineering and data visualization.
- Familiarity with big data technologies like Spark Hadoop or distributed computing frameworks.
- Handson experience with cloud platforms (e.g. AWS Azure GCP) and ML services.
- Proficiency in deploying ML models using tools such as Docker Kubernetes or similar.
- Strong analytical and problemsolving skills with attention to detail.
- Excellent communication skills and ability to work collaboratively in a team.
- Experience with NLP computer vision or timeseries data analysis.
- Knowledge of MLOps principles and tools for CI/CD in ML pipelines.
- Familiarity with version control systems like Git and experiment management tools.
- Background in statistical modeling deep learning or AI ethics.