Full Stack Developer_AI/ML Engineer

Rakuten Kobo Inc.; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Rakuten Kobo Inc. in Bengaluru is seeking a Full Stack Developer + AI ML Engineer to design, build, and operate production‑grade applications across backend, APIs, data workflows, and frontend components as needed.

You will contribute to cloud‑native and containerised solutions, develop AI/ML features, and ensure robust testing, release readiness, and operational stability of delivered software. Your work will span product collaboration, architecture decisions, and end‑to‑end delivery across

Qualifications

• : Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Electronics, Telecommunications, or a related field.
• : Strong software development skills with hands‑on experience in full‑stack engineering, including backend development, API design, system integration, data workflows, and frontend understanding where needed.
• : Strong understanding of distributed systems, microservices, containerisation, and scalable application deployment principles.
• : Proficiency in Python, Linux, shell scripting, Git, and modern software engineering practices for building production‑grade applications.
• : Hands‑on experience in AI/ML and data science, including exploratory data analysis, feature engineering, model development, model evaluation, and integration of AI/ML models into applications.
• : Hands‑on experience with Docker and Kubernetes, including container image creation, packaging, deployment workflows, CI/CD pipelines, automated testing, and operational support for cloud‑native systems.
• : Working knowledge of MLOps concepts such as model deployment, monitoring, versioning, and lifecycle management is preferred.
• : Analytical and problem‑solving skills; ability to understand business and technical problems, convert them into practical solution designs, and deliver scalable implementations with measurable impact.
• : Strong troubleshooting, critical thinking, attention to quality, performance, reliability, and end‑to‑end ownership.
• : Effective communication and collaboration; able to explain technical design, architecture choices, AI/ML logic, and deployment plans clearly to both technical and non‑technical audiences.

Responsibilities

• Design, develop, and enhance production‑grade applications using a full‑stack engineering approach spanning backend services, APIs, data workflows, microservices, and user‑facing components where required.
• Contribute to container‑based and cloud‑native solution design for scalable, secure, and resilient applications deployed across cloud or hybrid environments.
• Apply AI/ML, analytics, and data science techniques to solve business and network problems, including prediction, optimisation, anomaly detection, automation, and decision support use cases.
• Own unit testing, integration testing, system validation, release readiness, and deployment activities to ensure robust production delivery.
• Collaborate with product owners, platform teams, architects, QA teams, operations teams, and business stakeholders to drive end‑to‑end solution delivery.

Skills

Full‑stack engineering

Python

Linux

Docker

Kubernetes

AI/ML

Data analysis

CI/CD

Microservices

Testing

Communication

Education

Bachelor’s or Master’s in CS/Engineering/Data Science

Tools

Docker

Kubernetes

CI/CD

Git

Cloud platforms

Job description

Job Title

Full Stack Developer + AI ML Engineer

Location & Employment

Bangalore, India (Hybrid) – Full‑Time

Key Responsibilities

1. Design, develop, and enhance production‑grade applications using a full‑stack engineering approach spanning backend services, APIs, data workflows, microservices, and user‑facing components where required. Build clean, maintainable, and scalable software with strong engineering discipline, code quality, version control, reusable design patterns, and production readiness.
2. Contribute to container‑based and cloud‑native solution design for scalable, secure, and resilient applications deployed across cloud or hybrid environments. Build Docker images, work with Kubernetes deployment patterns, and support containerised application packaging, service integration, observability, and operational readiness for production systems. Apply practical MLOps practices for model packaging, deployment, monitoring, and lifecycle support as part of end‑to‑end solution delivery.
3. Apply AI/ML, analytics, and data science techniques to solve business and network problems, including prediction, optimisation, anomaly detection, automation, and decision support use cases. Work hands‑on with data analysis, feature engineering, model development, model evaluation, and integration of trained models and intelligent logic into software components.
4. Own unit testing, integration testing, system validation, release readiness, and deployment activities to ensure robust production delivery. Support field validation, troubleshooting, performance tuning, issue resolution, FOA activities, and production stabilization for delivered solutions.
5. Collaborate with product owners, platform teams, architects, QA teams, operations teams, and business stakeholders to drive end‑to‑end solution delivery. Continuously improve engineering practices, container deployment standards, testing methods, and reusable frameworks for faster and more reliable delivery.

Skills and Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Electronics, Telecommunications, or a related field.
• Strong software development skills with hands‑on experience in full‑stack engineering, including backend development, API design, system integration, data workflows, and frontend understanding where needed.
• Strong understanding of distributed systems, microservices, containerisation, and scalable application deployment principles.
• Proficiency in Python, Linux, shell scripting, Git, and modern software engineering practices for building production‑grade applications.
• Hands‑on experience in AI/ML and data science, including exploratory data analysis, feature engineering, model development, model evaluation, and integration of AI/ML models into applications.
• Hands‑on experience with Docker and Kubernetes, including container image creation, packaging, deployment workflows, CI/CD pipelines, automated testing, and operational support for cloud‑native systems.
• Working knowledge of MLOps concepts such as model deployment, monitoring, versioning, and lifecycle management is preferred.
• Analytical and problem‑solving skills; ability to understand business and technical problems, convert them into practical solution designs, and deliver scalable implementations with measurable impact.
• Strong troubleshooting, critical thinking, attention to quality, performance, reliability, and end‑to‑end ownership.
• Effective communication and collaboration; able to explain technical design, architecture choices, AI/ML logic, and deployment plans clearly to both technical and non‑technical audiences.

Preferred Qualifications

• Experience in telecom, network analytics, automation platforms, or AI‑driven operational use cases.
• Experience with RIC concepts, related application environments, or similar production platforms.
• Familiarity with cloud cost optimisation, security, governance, and production monitoring practices.
• Exposure to FOA, production rollout governance, event‑driven architectures, streaming pipelines, or platform integration patterns.