Software Engineer
AI in this role
Meet the Team
The Collaboration Technology Group is redefining the future of teamwork, building services that connect people effortlessly across devices, locations and time zones.
Our team in Stockholm builds, runs, and continuously improves the core media platform services behind Cisco’s collaboration products, operating at global scale across numerous datacentres. We’re a passionate, collaborative team focused on real-time media reliability, innovation, and engineering excellence.
Your Impact
As a Software Engineer in our Stockholm Webex Media (WxMedia) & SRE organization, you will bring new real-time media features to life and drive resolution for high-impact media platform escalations. You will combine Site Reliability Engineering practices with real-time media streaming architectures and modern agentic AI (reusable Skills, Model Context Protocol (MCP), and LLM tooling) to transform how we monitor, diagnose, and auto-remediate global media services handling billions of audio and video minutes.
What You'll Do
- Media Escalations & Real-Time Reliability: Lead deep-dive troubleshooting for complex WxMedia platform issues, investigating packet loss, jitter, latency, audio/video degradation, and SIP/WebRTC signaling failures across hybrid cloud and bare-metal environments.
- Real-Time Media Delivery: Bring new real-time media features to life based on product requirements, ensuring high performance, scalability, and seamless global routing.
- Technical Design & Architecture: Design and implement high-resilience software systems for AI-assisted observability, automated incident response, and self-healing cloud media infrastructure.
- Agentic Workflows & Tooling: Design, build, and maintain production-grade AI agents, MCP tool integrations, and deterministic evaluation pipelines for automated operational decision support.
- Telemetry & Insights: Implement ingestion and correlation pipelines across distributed logs, metrics, OpenTelemetry traces, change events, and runbooks to accelerate Mean Time to Detection (MTTD) and Resolution (MTTR).
- Safe Production Automation: Develop proactive anomaly detection and Human-in-the-Loop (HITL) remediation workflows with meticulous safety, security, and quality guardrails.
- Reliability & Scalability Engineering: Partner with application and infrastructure teams to define SLIs/SLOs, handle error budgets, and lead investigation post-incident reviews (PIRs).
- Mentorship & Collaboration: Mentor mid-level and junior engineers, conduct thorough code reviews, establish engineering standards, and drive operational excellence across global development and operations teams.
- Cross-Team Delivery: Manage priorities and timelines, communicate progress clearly, and work across teams to turn production needs into reliable software and AI-assisted capabilities.
Minimum Qualifications
- Bachelor’s degree + 8years of related experience, Master’s + 6 years, or PhD + 3 years in Computer Science, Software Engineering, or a related technical field.
- Consistent record as a Senior / Lead SRE or Software Engineer delivering distributed, high-availability SaaS or media platforms at scale.
- Experience with media streaming and relevant protocols for real-time applications (e.g., RTP/RTCP, SRTP, WebRTC, SIP, SDP, RTSP, or HLS/DASH).
- Strong proficiency in Python, Go, C++, or Java with experience designing microservices, APIs, and production automation.
- Deep experience with Kubernetes, Docker, and container orchestration in large-scale multi-cluster environments.
- Experience in SRE practices: SLI/SLO design, observability platforms (metrics/logs/traces), incident management, and automated root cause analysis (RCA).
Preferred Qualifications
- Experience with public cloud platforms (AWS, Microsoft Azure, or Google Cloud).
- Experience with close-to-real-time systems and performance-sensitive code paths.
- Comfort with incremental development, testing, and A/B testing in production.
- Steers AI effectively via context and prompt design.
- Experience building LLM pipelines, AI Agents, Model Context Protocol (MCP) servers/clients, RAG architectures, and evaluation frameworks.
- Experience with OpenTelemetry (OTel), Prometheus, Grafana, Splunk, ThousandEyes, or distributed tracing systems.
- Expertise in Terraform/IaC and GitOps/CI/CD pipelines (Jenkins, GitHub Actions, Harness).
- Experience implementing responsible AI guardrails, deterministic fallback logic, and policy-driven remediation engines.
- Experience with streaming and data platforms (Kafka, Redis, PostgreSQL, Elasticsearch/Vector DBs).
Collabhiring
Why Cisco?
At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.
Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.
We are Cisco, and our power starts with you.
How we rate this
Software Engineer at Cisco rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Describe a typical day in a role like this one: which parts run through AI directly?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, RAG, AI Agents, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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