About the role:

We are looking for an experienced Machine Learning Architect/Lead Engineer to design, optimize, and deploy advanced ML solutions that deliver real-world business impact. In this role, you will lead the full lifecycle of ML projects, from ideation to production, while mentoring junior engineers and collaborating with cross-functional teams. The ideal candidate brings deep expertise in Python, modern ML frameworks, and cloud platforms, with strong leadership and communication skills to drive scalable, high-quality solutions.

Responsibilities: 

• Architect and optimize advanced machine learning models, ensuring alignment with business goals and real-world applicability 
• Lead the development and deployment of scalable machine learning solutions, managing the full lifecycle from ideation to production 
• Mentor junior engineers, providing guidance and support to enhance team performance and technical prowess 
• Design and implement robust data pipelines for smooth integration and transformation of data used in modeling 
• Collaborate closely with cross-functional teams to integrate machine learning capabilities into existing systems and workflows 
• Evaluate and select suitable machine learning frameworks and tools that best fit project requirements 

What are we looking for: 

• Strong academic background in Computer Science, Engineering, Data Science, or related field (MSc/PhD preferred) 
• 5+ years of experience in machine learning engineering, with a strong track record of delivering production-grade ML solutions 
• Advanced proficiency in Python and ML frameworks such as TensorFlow and PyTorch 
• Deep understanding of machine learning algorithms (deep learning, ensemble methods, reinforcement learning, etc.) 
• Extensive experience with deploying machine learning models in cloud environments such as AWS, Azure, or Google Cloud 
• Exceptional communication skills, with the ability to convey technical information clearly to diverse audiences 
• Strong leadership skills with experience in managing and developing technical teams 
• Ability to evaluate trade-offs between accuracy, latency, interpretability and cost 

Nice-to-have: 

• Experience with MLOps practices, including model lifecycle management and automation (using tools like MLflow or Kubeflow) 
• Familiarity with distributed computing frameworks and technologies (such as Apache Spark or Kubernetes) 
• Experience in the financial services industry, with a focus on integrating AI-driven solutions to enhance service offerings
• Knowledge of ethical AI practices and regulatory requirements in AI deployment and usage