ML Solutions Architect

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Requirements:

• Possess a minimum of 10 years of experience in machine learning engineering or software architecture roles, with a focus on designing and implementing ML solutions.
• Demonstrate expertise in machine learning algorithms, model development, and evaluation techniques, with hands-on experience in building end-to-end ML systems.
• Have proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow or PyTorch.
• Exhibit strong problem-solving and analytical skills, with the ability to tackle complex technical challenges and drive innovative solutions.
• Possess excellent communication and collaboration skills, with the ability to effectively interact with cross-functional teams and stakeholders.
• Show a passion for self-driven learning and staying updated on advancements in machine learning technologies, with a keen interest in applying them to Conversational AI and product development initiatives.

Responsibilities:

• Collaborate with product management and engineering teams to understand business requirements and translate them into scalable ML architectures.
• Design end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment.
• Implement state-of-the-art machine learning algorithms and models to address specific use cases in Conversation AI and Nebula product projects.
• Lead technical discussions and provide guidance on best practices for ML system design, ensuring scalability, reliability, and maintainability.
• Work closely with data engineers to optimize data pipelines and infrastructure for efficient model training and inference.
• Stay updated on the latest advancements in machine learning technologies and incorporate emerging techniques and frameworks into our ML solutions.