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.