AI/ML Engineer

About the Role

We are seeking a highly skilled AI/ML Engineer to design, deploy, and scale machine learning systems in production environments. This role focuses on building robust ML pipelines, optimizing model performance, and ensuring reliable, scalable infrastructure that powers real-world AI applications.

You will work closely with data scientists, data engineers, and cross-functional stakeholders to transform research models into production-ready solutions that drive business impact.

Must Have

●       Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn

●       Hands-on experience deploying ML models in cloud environments (AWS, Google Cloud Platform, or Azure)

●       Experience with containerization (Docker) and orchestration tools like Kubernetes

●       Expertise in MLOps practices, including CI/CD, model versioning, monitoring, and retraining

●       Strong understanding of distributed systems and scalable ML infrastructure

●       Solid knowledge of machine learning algorithms, training, and evaluation techniques

●       Experience collaborating with cross-functional teams (data science, engineering, business)

●       Strong debugging and problem-solving skills in production environments

Experience

●       Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)

●       Proven experience building and deploying production-grade machine learning systems

●       Experience working in cloud-native environments supporting scalable ML workflows

Your Role

●       Design and implement end-to-end ML pipelines for model training, deployment, and inference

●       Build and optimize scalable infrastructure for machine learning workflows

●       Collaborate with data scientists to productionize research models

●       Optimize models for latency, accuracy, and efficiency in real-world environments

●       Develop and maintain automated workflows for versioning, monitoring, and retraining

●       Partner with data engineering teams to ensure high-quality data availability

●       Build tools to streamline ML development and deployment processes

●       Ensure adherence to responsible AI principles, including safety and reliability

Outcomes

●       Deliver scalable, production-ready ML pipelines that support real-time and batch use cases

●       Enable seamless transition from model development to deployment

●       Improve model performance and reliability through continuous monitoring and optimization

●       Establish robust MLOps frameworks for lifecycle management and automation

●       Ensure high-quality data pipelines supporting ML workflows

●       Drive adoption of best practices in ML engineering and deployment