Machine Learning Engineer

Role Overview : As a Machine Learning Engineer, you will be responsible for designing, deploying, and maintaining production-grade models that drive our core product features. You will work closely with cross-functional teams, including data scientists, backend engineers, and product managers, to translate business requirements into robust technical architectures. Your work will directly impact how our clients process large-scale data, ensuring that our machine learning pipelines are efficient, scalable, and capable of delivering real-time insights that improve business outcomes. Key Responsibilities : - Architect and implement end-to-end machine learning pipelines to automate data processing and model training for enterprise clients. - Develop and optimize deep learning models and computer vision algorithms to enhance the accuracy and performance of our core diagnostic platforms. - Integrate Large Language Models into existing workflows to improve natural language understanding and automated response capabilities for end-users. - Collaborate with engineering teams to deploy models into production environments, ensuring high availability and low-latency performance. - Monitor model performance in production, implementing feedback loops to refine algorithms and maintain high standards of predictive accuracy. - Document technical processes and model architectures to facilitate knowledge sharing and maintain code quality across the engineering department. Required Skillset : - 4 to 9 years of hands-on experience in building and scaling machine learning systems. - Demonstrated expertise in Python for developing complex machine learning applications and data processing scripts. - Proven ability to design and implement deep learning architectures and computer vision solutions using frameworks like PyTorch or TensorFlow. - Strong understanding of LLM integration, including fine-tuning techniques and prompt engineering for enterprise-scale applications. - Ability to communicate complex technical concepts to non-technical stakeholders, ensuring alignment on project goals and deliverables. - Experience working in a remote-first environment, demonstrating self-discipline and the ability to collaborate effectively across distributed teams.