AI/ML Architect

Requirements

1. 4-5 years of experience in AI/ML roles, with at least 1-2 years contributing to architecture design and leading technical initiatives.
2. Strong skills in Python or R, with strong knowledge of frameworks like TensorFlow, PyTorch, and scikit-learn.
3. Proven ability to work with cloud platforms (Google AI Platform, Nvidia AI Enterprise, Azure Machine Learning) and deploy solutions on-premise.
4. Expertise in building and optimizing data workflows, from ingestion and transformation to preprocessing and feature engineering for ML pipelines.
5. Strong understanding of supervised, unsupervised, and reinforcement learning techniques.
6. Knowledge of ML-Ops practices, including automating model training, deployment, and monitoring in production environments.
7. Experience with Auto-ML technologies and techniques for automating model selection, training, and tuning.
8. Proven ability to deliver AI/ML solutions in production environments and real-world projects.
9. Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
10. Strong communication skills to effectively convey complex data findings to non-technical stakeholders.

Responsibilities

1. Define the organization's AI/ML strategy and roadmap, ensuring alignment with business objectives.
2. Design scalable and efficient AI/ML architectures, integrating data ingestion pipelines, preprocessing workflows, model training, and deployment processes.
3. Identify opportunities for innovation and efficiency improvements in the AI/ML ecosystem.
4. Establish and manage ML-Ops pipelines for continuous integration, delivery, and monitoring of machine learning models in production environments.
5. Responsible for mentoring data scientists and engineers, deepening their expertise in AI/ML principles and ML-Ops practices.
6. Collaborate with other stakeholders to integrate ML solutions into production systems.
7. Evaluate and introduce cutting-edge technologies (e.g., AutoML, serverless ML deployments) to improve efficiency and scalability.