Requirements
- 4-5 years of experience in AI/ML roles, with at least 1-2 years contributing to architecture design and leading technical initiatives.
- Strong skills in Python or R, with strong knowledge of frameworks like TensorFlow, PyTorch, and scikit-learn, etc.
- Proven ability to work with cloud platforms (Google AI Platform, Nvidia AI Enterprise, Azure Machine Learning, etc) and deploy solutions on-premise.
- Expertise in building and optimizing data workflows, from ingestion and transformation to preprocessing and feature engineering for ML pipelines.
- Strong understanding of supervised, unsupervised, and reinforcement learning techniques.
- Knowledge of ML-Ops practices, including automating model training, deployment, and monitoring in production environments.
- Experience with Auto-ML technologies and techniques for automating model selection, training, and tuning
- Proven ability to deliver AI/ML solutions in production environments and real-world projects.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- Strong communication skills to effectively convey complex data findings to non-technical stakeholders.
Responsibilities
- Define the organization's AI/ML strategy and roadmap, ensuring alignment with business objectives
- Design scalable and efficient AI/ML architectures, integrating data ingestion pipelines, preprocessing workflows, model training, and deployment processes
- Identify opportunities for innovation and efficiency improvements in the AI/ML ecosystem
- Establish and manage ML-Ops pipelines for continuous integration, delivery, and monitoring of machine learning models in production environments
- Responsible for mentoring data scientists and engineers, deepening their expertise in AI/ML principles and ML-Ops practices
- Collaborate with other stakeholders to integrate ML solutions into production systems.
- Evaluate and introduce cutting-edge technologies (e.g., AutoML, serverless ML deployments) to improve efficiency and scalability