ML Solutions Architect

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.

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.