Introduction
We are looking for 5+years experienced candidates for this role.
Job Description
- Strong knowledge of statistical modeling, machine learning, deep learning and GenAI.
- Proficiency in Python and experience optimizing code for performance.
- Experience with data preprocessing, feature engineering, data visualization and hyperparameter tuning.
- Solid understanding of database concepts and experience working with large datasets.
- Experience deploying and scaling machine learning models in a production environment.
- Familiarity with machine learning operations (MLOps) and related tools.
- Good understanding of Generative AI concepts and LLM finetuning.
- Excellent communication and collaboration skills.
- Bachelor's or Master's degree in a quantitative field such as statistics, mathematics, computer science, or a related area.
Responsibilities include:
- Lead the development and deployment of machine learning/deep learning models to address key business challenges.
- Apply statistical modeling, data preprocessing, feature engineering, machine learning, and deep learning techniques to build and improve models.
- Have expertise in at least two of the following areas: computer vision, predictive analytics, natural language processing, time series analysis, recommendation systems.
- Expertise in GenAI models is a must.
- Design, implement, and optimize data pipelines for model training and deployment.
- Experience with any one of the model serving frameworks (e.g.,TensorFlow Serving, TorchServe, KServe, or similar).
- Design and implement APIs for model serving and integration with other systems.
- Collaborate with cross-functional teams to define project requirements, develop solutions, and communicate results.
- Mentor junior data scientists, providing guidance on technical skills and project execution.
- Stay up-to-date with the latest advancements in data science and machine learning, particularly in generative AI, and evaluate their potential applications.
- Communicate complex technical concepts and analytical findings to both technical and non-technical audiences.