• Design, develop, and operate scalable backend services and microservices using modern Python (3.11+). • Build high-performance, well-documented RESTful and/or async APIs (FastAPI / Flask), with attention to latency, throughput, and reliability. • Write clean, typed, reusable, and testable code, backed by automated tests (pytest) and meaningful coverage. • Design systems for high availability, horizontal scalability, and low latency; reason about failure modes, backpressure, and graceful degradation. • Integrate and serve AI/ML capabilities — model/LLM API integration, retrieval pipelines (RAG), vector search, and inference endpoints — in collaboration with Data Science. • Integrate relational, NoSQL, caching, and vector databases, plus cloud data warehouses (Snowflake) and cloud-based storage. • Instrument services for observability (structured logging, metrics, tracing) and own production troubleshooting and performance optimization. • Implement security best practices: authentication, authorization, secrets management, and data protection. • Participate in architecture discussions, design reviews, and code reviews; help define and uphold engineering standards. • Mentor junior engineers and contribute to a strong engineering culture. • Work with DevOps / Platform teams on CI/CD pipelines, infrastructure-as-code, and deployment automation.

• Develop high-quality Go (and eBPF) code for the Tetragon project • Extend Tetragon capabilities to mitigate security vulnerabilities • Enhance Tetragon to better understand and control the execution flow of native (e.g., written in C/C++) or runtime-based (Java, Python, Go, etc.) applications • Enhance Tetragon capabilities to filter malicious network packets that can trigger vulnerabilities in applications/systems • Enhance Tetragon, and potentially the kernel and the eBPF sub-system, to mitigate against kernel-level vulnerabilities • Design and optimize systems to deploy and manage Tetragon across large fleets, in K8s or other environments (VMs) • Architect infrastructure that converts raw event data into intelligence streams with minimal performance overhead • Partner across teams to align requirements, define integration points, and deliver production-ready solutions • Contribute to open-source projects and represent Cisco Isovalent in community discussions • Participate in architecture reviews, code reviews, and documentation

• Design self-contained software-engineering problems for AI agents to solve. • Build and configure development environments using Docker. • Write automated, deterministic tests to reliably grade AI performance. • Create accurate reference solutions for complex coding challenges.

• Design and maintain versioned data pipelines that handle the ingestion and conflation of complex retail data. • Ensure that data outputs surfaced to consumer-facing dashboards meet a high quality bar for accuracy and reliability. • Implement a "right tool for the job" approach—building robust rules-based normalization and RegEx logic for bounded tasks, while strategically integrating LLMs. • Architect solutions that are mindful of the massive scale of retail environments. • Manage the lifecycle of data schema and pipeline logic to allow for rapid iteration without breaking downstream consumer insights.