AI​/ML Architect

As an AI/ML Architect, you will play a crucial role in designing and implementing robust, scalable, and efficient AI/ML solutions for our diverse range of projects. The successful candidate will be responsible for understanding business requirements, defining architecture, and leading the development of AI/ML models that drive innovation and enhance business outcomes.

• Collaborate with cross-functional teams to understand business objectives and requirements.
• Design end-to-end AI/ML architectures that align with business goals and scalability requirements.
• Evaluate and select appropriate technologies and frameworks for implementation.
• Lead the development and implementation of state-of-the-art AI/ML models.
• Utilize Python and other relevant tools to build and optimize machine learning algorithms.
• Ensure models are accurate, efficient, and scalable for deployment in production environments.
• Work closely with data engineers to ensure the availability of high-quality, clean, and relevant data.
• Develop and implement data pre-processing and feature engineering pipelines.
• Implement monitoring systems to track the performance of deployed models.
• Identify opportunities for optimization and improvement in model accuracy and efficiency.
• Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders.
• Document architecture, design decisions, and code to ensure knowledge transfer and maintainability.

• Master's or Ph.D. in Computer Science, Data Science, or related field.
• Proven experience as an AI/ML Architect with a focus on Python-based solutions.
• Strong expertise in machine learning frameworks such as Tensor Flow, PyTorch, or scikit-learn.
• Experience with designing and implementing end-to-end machine learning pipelines.
• Proficiency in data processing, feature engineering, and model deployment.
• Excellent communication skills and the ability to collaborate effectively with cross-functional teams.
• Demonstrated problem-solving and critical-thinking skills.

• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of containerization technologies (Docker, Kubernetes).
• Familiarity with deep learning techniques and frameworks.
• Previous experience in industries such as healthcare, finance, or manufacturing is a plus.