Director, Software Engineering | Enterprise AI Platforms, Infrastructure & Automation

Meet the Team

In Cisco Enterprise AI Platforms & Infrastructure (EAIPI), our vision is to architect, build, and scale world-class agentic AI platforms, foundation infrastructure, and developer ecosystems that power Cisco's next generation of intelligent products and services. We are the engineering organization behind Cisco's flagship AI initiatives, including MyAgent (Cisco's super agent) and the Circuit Enterprise AI platform.

Our high-velocity team operates at the intersection of frontier generative AI, distributed systems, GPU infrastructure, and modern software engineering. We build with a steadfast focus on engineering craftsmanship, high talent density, principled quality, and developer enablement—empowering thousands of engineers across Cisco to create, validate, and scale multimodal agentic experiences.

Your Impact

As the Director of AI Support & Automation, you will build and lead our Bangalore-based engineering pod as an elite 'player-coach' calibrated two levels below the VP. Combining direct architectural leadership with deep, hands-on systems development across MyAgent and Circuit, you will set the benchmark for AI platform reliability and quality.

You will enable the engineering organization to improve and optimize engineering processes and infrastructure, directing software systems development, application engineering, and software quality assurance for cloud-based and agentic AI platforms. Your directive spans across 5 core pillars:

·       Pillar 1: Tier 3 Engineering & Escalated Platform Support: Act as the ultimate engineering escalation authority for MyAgent and Circuit. Lead hands-on debugging, root-cause forensic analysis, and kernel/runtime patch development for complex production failures, memory leaks, context bleed, and tool execution anomalies. Bridge the gap between SRE/L2 operational support and core platform architecture with zero-regression hotfix.

·       Pillar 2: Premier AI Agent Test Harness & Evaluation: Architect and build an end-to-end, agent-driven test automation harness. Implement automated LLM-as-a-judge scoring frameworks, multi-agent simulation loops, deterministic task verification, and golden dataset benchmarking. Maintain uncompromising quality gates with >99% test pass rates across complex functional, regression, experimental, and safety workflows.

·       Pillar 3: AI Software & GPU Performance Validation: Lead performance profiling, stress testing, and concurrency load and latency validation across dedicated on-premise GPU clusters and cloud runtimes. Optimize latency, token throughput, context cache utilization, and resource efficiency.

·       Pillar 4: Modular SDK & API Developer Platform: Deliver modular, embeddable SDKs, React components, and headless APIs that empower thousands of Cisco software engineers to control, customize, and extend multimodal agentic experiences.

·       Pillar 5: High-Talent-Density Team Leadership: Recruit, mentor, and lead a small, high-density pod (6–10 elite systems, AI, and test automation engineers) in Bangalore. Lead by example through daily code reviews, technical design RFCs, pair programming, and rigorous engineering craftsmanship.

• Technical Architecture & Testing Methodology:

·       Deterministic Harness Layer: Unit and integration verification of MCP protocol compliance, tool schema validation, state machines, token exchange (RFC 8693), and memory storage persistence.

·       LLM-as-a-Judge Evaluation Framework: Automated scoring pipelines using calibrated frontier and on-premise judge models to evaluate grounding, factual accuracy, safety guardrails, hallucination rates, and task completion against versioned golden sets.

·       Multi-Agent Simulation & Adversarial Red-Teaming: Automated agent swarms generating synthetic edge cases, malicious prompt injections, tool misuse attempts, and context overflow scenarios to proactively identify failure modes.

·       Continuous Canary & Regression Pipeline: Automated staging deployment validation with automated rollback mechanisms, real-time latency tracing (OpenTelemetry), and semantic drift monitoring.

Minimum Qualifications

·       Education: Bachelor's degree (B.Tech / B.E. / B.S.) in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Artificial Intelligence, or a related quantitative STEM discipline from an accredited institution.

·       Professional Experience: 10+ years of progressive software engineering experience, including 3+ years leading high-performing technical engineering teams and 2+ years of hands-on production experience with LLMs, agentic workflows, or AI platforms.

·       Hands-on Coding Proficiency: Expert-level fluency in Python and modern TypeScript/JavaScript; strong working proficiency in Go, C++, or Rust for systems-level performance tuning.

·       AI & Systems Architecture: Deep practical experience with Model Context Protocol (MCP), LangGraph, LlamaIndex, vLLM, Triton Inference Server, PyTorch, and vector databases.

·       Modern Test & Performance Engineering: Demonstrated track record of designing large-scale distributed test harnesses, performance testing tools (k6, Locust, JMeter), browser automation (Playwright), and AI evaluation frameworks (Ragas, DeepEval, Promptfoo, Eval Studio).

Preferred Qualifications

·       Advanced Credentials: Master's degree (M.Tech / M.S.) or Ph.D. in Computer Science, Artificial Intelligence, Distributed Systems, Machine Learning, or Software Engineering from a premier tier-1 engineering institution or top global research universities).

·       GPU & Infrastructure Acumen: Solid understanding of GPU compute infrastructure, CUDA memory management, high-density server configurations, and container orchestration (Kubernetes, Docker).

·       Academic, Research, or Open Source Contributions: Demonstrated academic depth or research contributions in distributed systems, compiler optimization, GPU parallel computing (CUDA/Triton), formal software verification, major contributions to tier-1 open-source AI frameworks/benchmarks, or recognized technical patents in AI/systems engineering.

·       Player-Coach Leadership Archetype: Proven ability to lead from the front, write production code, conduct deep architectural reviews, and inspire an elite engineering culture without relying on bureaucratic middle management.

·       Enterprise Executive Communication: Excellent analytical, written, and verbal communication skills with the ability to influence, interact, and present technical architectures effectively across engineering, product, and executive leadership.

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. 

Disclaimer

To ensure that we hire the best talent in the right way, we follow a strict hiring process and recently, Cisco has been made aware of fraudulent recruiters claiming to be from the company. Please be advised that any communication from Cisco about careers will:

• be in direct response to an application you have submitted through the company career site
• begin with screening or an interview
• originate from a Cisco email address, and
• be conducted across email, phone, or WebEx

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