Mistral AISingapore4h ago
CiscoPosted 1mo ago
Software Engineer (Agentic AI, LLM, Python | 5 to 8 Years) at Cisco scores 89 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
Develops software consistent with Cisco ‘Design Thinking Principles’ with a focus on simplification and UX (User Experience) at its core, using secure coding practices, ensuring user privacy, and following software development best practices. Partners with other teams including design and product management to create the right solution for the customers. Creates technical design documentation to be used by the team as well as contributing to user documentation to be used by end users. Debugs and addresses software issues during development and in production systems to support customers. Brings new ideas for product innovation and helps improve software development processes.
What You’ll Do:
Align to overall architecture and technical design for backend, data, cloud, and AI platforms.
• Lead the design of scalable Python backend systems and data pipelines.
• Drive architecture and development of complex Agentic AI/LLM systems using LangGraph.
• Define strategies for LLM performance, token, latency, and cost optimization.
• Establish standards for AI/RAG architecture, APIs, databases, and distributed systems.
• Drive AWS and cloud-native architecture using S3, SQS, SNS, Lambda, Docker, and Kubernetes.
• Lead complex technical and production issues across teams.
• Mentor engineers and influence engineering standards and technical direction.
• Partner with stakeholders to align technical strategy with business goals.
Minimum Qualifications:
Minimum 5 to 10 years of hands-on experience in machine learning engineering, backend development, and applied AI.
Deep experience with Agentic AI, LLM applications, LangGraph, and RAG architectures.
Proven experience operationalizing deep learning and LLM models in production.
Proven experience deploying LangGraph and multi-agent workloads with versioned prompts, runtime safeguards, and rollback strategy.
Strong experience defining and implementing token, latency, throughput, and cost optimization strategies for LLM systems.
Experience with LLM evaluation and observability tooling (for example, LangSmith or equivalent).
Expertise in ETL optimization, including workload-aware scheduling, partition pruning, caching, and storage-format tuning.
Strong expertise in Python, backend engineering, and distributed systems
Preferred Qualifications
Demonstrated ability to mentor engineers and lead large-scale, cross-functional initiatives.
Proven ability to align technical strategy with business goals and stakeholder priorities.
Strategic thinker with ability to translate complex requirements into scalable technical solutions.
Knowledge of network technologies and enterprise platform integration patterns.
Strong written and verbal communication, including executive-ready technical storytelling.
Experience integrating Model Context Protocol and related AI tool ecosystems.
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.
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 would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- What are the limits of LangGraph that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Rag, AI Evaluation, and LangGraph. 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.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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