Job Description
Key Responsibilities:
- Collaborate with UI and Java Backend teams to integrate new forecasting and regression models into the existing architecture
- Design, develop, and maintain the machine learning engine, focusing on optimizing model performance and accuracy
- Work closely with data scientists to refine and implement new machine learning algorithms for forecasting and regression tasks
- Troubleshoot and resolve complex issues related to model deployment, prediction, or inference
Model Deployment and Hosting:
- Design, develop, and deploy a scalable model hosting platform e.g. using Kubeflow, Kserve etc
- Configure and handle the platform for seamless model serving and inference
Required Machine Learning and AI Expertise:
- Currently working on machine learning projects and staying up-to-date with the latest trends and techniques
- Proven understanding of machine learning fundamentals, including supervised and unsupervised learning, deep learning, and natural language processing
- Experience with Generative AI (GenAI) technologies, such as text-to-image models or conversational AI is a plus
Technical Skills:
- Programming languages: Java, Python, or other relevant programming languages
- Machine Learning Libraries: StatsModel, Scikit-learn, TensorFlow, PyTorch etc
- Data Preprocessing: pandas, NumPy, scikit-learn etc
- Model Deployment: Docker, Kubernetes (MLflow/Kubeflow is plus)
- Cloud Computing Platforms: AWS, Azure, or private cloud platforms
- Experience with Java Spring Boot for building scalable web applications
Preferred Knowledge/Skills
- Previous experience as a Full Stack Developer with expertise in Java Spring Boot and Angular
- Strong understanding of web application architecture and scalability principles
- Excellent problem-solving skills and ability to work independently with R&D mindset
Good to Have:
- Experience with DevOps practices (e.g., Continuous Integration, Continuous Deployment) for machine learning models
- Certification in machine learning or data science
- Experience with cloud-based machine learning platforms (e.g., AWS SageMaker, Azure Machine Learning)
- GenAI exposure