SW Architect

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.

• Lead software requirements analysis and translate business, customer, system, and network-element needs into scalable technical solutions and product architectures.

• Define and evolve EMS architecture and component design, including APIs, interfaces, microservices, data flows, containers, Kubernetes/OpenShift, and cloud-native deployments.

• Provide hands-on technical leadership across development, prototyping, code/design reviews, debugging, performance optimization, and complex root-cause analysis.

• Build and enhance multi-agent pipelines and agent harnesses with coordinated agent roles, iterative planning and execution loops, context, tools, skills, memory, orchestration, validation, recovery, observability, and human approval gates.

• Drive AI-enabled SDLC adoption across requirements, specification, design, coding, review, testing, troubleshooting, defect correction, documentation, and delivery; continuously improve agent prompts, workflows, test harnesses, and validation based on performance and failure patterns.

• Lead technology modernization and engineering collaboration, driving adoption of Java, Spring Boot, microservices, containers, Kubernetes, OpenShift, automated testing, and CI/CD while mentoring teams and collaborating with product, architecture, platform, verification, productization, and network-element stakeholders.

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, Spring Boot, microservices, Docker, 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 SNMP, NETCONF/YANG, SFTP, 3GPP interfaces, REST APIs, 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.