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.