About the role
We are looking for a skilled senior ML engineer to design, develop, deploy, and maintain scalable AI/ML solutions on cloud platforms. The candidate will work closely with data scientists, engineering, and business teams to build production-grade AI systems, MLOps pipelines, APIs, and cloud-native applications.
Responsibilities:
- Design and implement scalable cloud solutions on AWS/Azure.
- Develop backend services, REST APIs, microservices, and serverless applications using Python.
- Build and manage MLOps pipelines for ML/GenAI model deployment and monitoring.
- Deploy and manage containerised applications using Docker and Kubernetes (EKS/AKS).
- Develop and maintain CI/CD pipelines using Jenkins, GitHub Actions, or Azure DevOps.
- Implement Infrastructure-as-Code using Terraform/ARM templates.
- Work with data scientists to productionise AI/ML models.
- Monitor application performance, reliability, security, and cloud cost optimisation.
- Troubleshoot deployment and infrastructure issues.
- Maintain technical documentation and deployment guidelines.
- Collaborate with cross-functional teams and mentor junior engineers.
Requirements:
- 3-5 years of experience in AWS and/or Azure.
- Strong programming experience in Python.
- Hands-on experience with Docker and Kubernetes, Terraform / ARM Templates, MLflow or similar MLOps tools, Jenkins / GitHub Actions / Azure DevOps, REST APIs and microservices.
- Knowledge of cloud security, networking, monitoring, and logging.
- Experience with scalable AI/ML production systems is preferred.
Preferred Qualifications:
- Bachelor's/Master's degree in computer science or a related field.
- AWS/Azure/Kubernetes certifications are an added advantage.
- Experience in consulting or enterprise-level projects is preferred.