SW Architect

Nokia; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Nokia is seeking an experienced Software Architect to define architecture and provide hands-on technical leadership for scalable, secure, cloud-native, and AI-driven Element Management System solutions within Autonomous Networks.

You will drive multi-agent pipelines, agent harnesses, AI-assisted SDLC, automated testing, CI/CD, and modern cloud-native technologies to improve engineering productivity and delivery speed.

Qualifications

• Must have a BE/BTech or equivalent with 10+ years in software architecture and distributed systems.
• Hands-on experience with Java, Python, Kubernetes, APIs, CI/CD, automated testing and cloud-native architecture.
• Experience building multi-agent solutions and orchestrated pipelines with robust controls.

Responsibilities

• Define architecture for scalable, secure EMS solutions in cloud-native environments.
• Provide hands-on technical leadership and drive requirements analysis and system design.
• Lead development, troubleshooting, performance optimization, and key technical decisions.

Skills

Java

Python

Kubernetes

APIs

CI/CD

Automated testing

Cloud-native architecture

Requirements analysis

Distributed systems

Architecture design

Education

Bachelor's or Master’s in Engineering

Tools

OpenShift

Kafka

Helm

GitOps

Kubernetes Operators

Job description

Job Summary

We are seeking an experienced Software Architect to define architecture and provide hands-on technical leadership for scalable, secure, cloud-native, and AI-driven Element Management System solutions within Autonomous Networks. The role involves leading requirements analysis, system design, development, troubleshooting, performance optimization, and key technical decisions while collaborating closely with product, engineering, architecture, and platform teams. You will drive the adoption of multi-agent pipelines, agent harnesses, AI-assisted SDLC, automated testing, CI/CD, and modern cloud-native technologies to improve engineering productivity, software quality, and delivery speed. This is an opportunity to shape next-generation telecommunications software and engineering practices as networks evolve toward increasingly autonomous operations.

Our EMS team builds advanced network management solutions that help telecom operators manage and optimize complex mobile networks. We are transforming toward an AI-native, cloud-first organization through agentic AI, automation, and modern engineering practices across MantaRay NM.

How You Will Contribute and What You Will Learn

Qualifications

Must have:

• Bachelors' / Masters' Engineering degree with 10+years of experience in software architecture, requirements analysis, design, development, and troubleshooting of large-scale distributed systems, with expertise in Java, Python, Kubernetes, APIs, CI/CD, automated testing, and cloud-native architecture.
• Hands-on experience building multi-agent solutions, agent squads/swarms, and orchestrated pipelines with iterative execution, feedback loops, planning, validation, and execution controls.
• Understanding of agent harness engineering, including context management, tool integration, skills/rules, memory, planning, critic/reviewer patterns, observability, error handling, validation, and human-in-the-loop mechanisms.
• Experience integrating agentic workflows with source repositories, issue trackers, code reviews, build pipelines, test environments, knowledge bases, and engineering tools, while ensuring pipelines are repeatable, testable, idempotent, secure, traceable, and scalable.

Good to have:

• Experience in Telecommunications OSS/EMS/NMS, network management, element management, or related telecom software domains.
• Knowledge of telecom and cloud technologies including interfaces OpenShift, Kafka, Helm, GitOps, and Kubernetes Operators.
• Experience with specification-driven development, RAG workflows, knowledge-base creation, automated defect triage and fixing, code migration, CI pipeline analysis, and lab-integrated testing.
• Understanding of Responsible AI practices, including security, data protection, human oversight, output validation, auditability, quality controls, and safe deployment of agentic systems.