Role Overview :

As a Machine Learning Engineer, you will lead the design and deployment of scalable AI models that drive decision-making for our enterprise clients. You will work closely with data scientists, cloud architects, and product stakeholders to bridge the gap between experimental research and production-grade software. Your work will directly influence the efficiency of our clients' data pipelines and the sophistication of their automated business processes, ensuring that our AI implementations are both robust and high-performing.

Key Responsibilities :

- Architect and deploy end-to-end machine learning pipelines to automate complex business workflows for our global clientele.

- Develop and fine-tune generative AI and agentic AI models to solve specific industry challenges, enhancing the intelligence of client platforms.

- Integrate advanced data solutions within the Microsoft Fabric ecosystem to ensure seamless data flow and model accessibility.

- Optimize deep learning models using PyTorch and TensorFlow to improve inference speed and accuracy in production environments.

- Collaborate with cross-functional teams to translate business requirements into technical specifications, ensuring alignment with project goals.

- Maintain high standards of code quality and system reliability through rigorous testing and continuous integration practices.

Required Skillset :

- Demonstrated expertise in Python for building production-ready machine learning systems and data processing scripts.

- Advanced proficiency in Deep Learning frameworks including PyTorch and TensorFlow, with a proven track record of deploying models at scale.

- Hands-on experience with Azure cloud services and Microsoft Fabric to manage data storage, compute, and model deployment.

- Deep understanding of Generative AI and Agentic AI architectures, with the ability to implement autonomous agents that perform complex tasks.

- Strong analytical and problem-solving skills, with the ability to communicate technical concepts clearly to non-technical stakeholders.

- Proven ability to thrive in a remote work environment, demonstrating self-discipline, proactive communication, and effective time management.

- A minimum of 6 - 8 years of professional experience in machine learning engineering or a related data science field.