Back
Tier-1 brand, generalist fullstack title, metro location, and broad skillset increase candidate competition.
Fullstack plus ML skills are transferable across industries, though telecom domain preference adds moderate specialization.
Broad mandatory technical stack (Python, ML, Docker, Kubernetes) but no explicit years makes filters moderately strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop full-stack, production-grade AI-driven telecom applications including backend services, APIs, data workflows, and microservices.
Build and support containerized deployment workflows using Docker and Kubernetes with an emphasis on operational readiness and practical MLOps practices.
Own testing, deployment, production support, and collaborate cross-functionally to deliver scalable, maintainable software solutions.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Electronics, Telecommunications, or related field.
Strong hands-on experience in full-stack development with proficiency in Python, Linux, shell scripting, Git, and modern software engineering practices.
Hands-on experience with Docker and Kubernetes for container image creation, packaging, deployment workflows, and operational support in cloud-native or hybrid environments.
Work Experience Required: Not explicitly mentioned in the JD.
Engineer with strong full-stack software development background and practical AI/ML model development and integration skills.
Experienced with container-based cloud-native architectures and familiar with MLOps concepts including model deployment and lifecycle management.
Comfortable owning end-to-end solution delivery in cross-functional teams within telecom or related technical domains.