Staff Software Engineer- Eng

Technical Excellence & Architecture - Design and implement complex AI-powered systems, establishing architectural patterns for agent orchestration, LLM integration, and multi-agent workflows that serve as organizational standards. - Be hands-on with code, maintaining high technical standards through direct implementation of core features, technical spike work, and complex problem-solving. - Ensure high standards of performance, scalability, reliability, and observability when architecting and developing AI systems, making trade-off decisions across safety, transparency, and compliance. - Establish and enforce engineering best practices across the team (e.g., documentation, automation, code management, security principles, responsible AI development). - Proactively identify and address technical debt, service health issues, and incidents. Utilize service health indicators and telemetry to drive action and implement operational excellence practices. - Design reliable, safe, and observable AI systems, setting standards for multi-agent orchestration, tool and skill design, and prompt engineering practices across the organization. - Contribute significantly to the R.I.D.E. (Recommend, Inform, Decide, Execute) framework as a technical expert and decision-maker. - Demonstrate deep understanding of CI/CD pipelines from build, test, to deploy phases, and champion best practices in deployment strategy and risk management. Technical Influence & Collaboration - Influence architectural and technical decisions across teams, advocating for high standards in systems operating at the intersection of software and AI, particularly for probabilistic and non-deterministic systems. - Foster a culture of service ownership and technical excellence through code reviews, technical discussions, and collaborative problem-solving. - Drive engineering efficiency and quality through established patterns and best practices, promoting continuous improvement and innovation in development processes. - Promote a growth mindset and drive organizational technical excellence, championing new technologies, frameworks, and responsible AI practices where appropriate. - Lead cross-functional technical collaboration with product management, design, and operations to ensure alignment on technical feasibility, architectural decisions, and successful project delivery. - Champion DevOps principles and full-stack ownership, demonstrating how to operate from requirements gathering through design, development, testing, deployment, and production support. - Mentor engineers at all levels through code reviews, technical pairing, and knowledge sharing, fostering a culture of learning, collaboration, and continuous improvement. - Share technical expertise and provide guidance on complex problems, helping engineers develop skills in AI systems, agent design, and responsible AI practices. - Stay current with LLMs, agent frameworks, AI evaluation techniques, and responsible AI practices, advocating for their adoption and establishing organizational standards that drive innovation and productivity. Execution Excellence - Drive the planning and execution of high-impact, complex technical projects, ensuring quality delivery aligned with business goals and timelines. Navigate technical tradeoffs and manage risks effectively. - Align technical work with strategic direction, understanding business context and ensuring technical decisions support company objectives. - Demonstrate engineering excellence and service ownership, including cost-conscious architecture, quality management, and proactive production support. - Collaborate effectively with cross-functional teams (product management, design, operations) to ensure technical feasibility, alignment, and successful delivery. Communicate technical concepts clearly to stakeholders at all levels. - Drive delivery of AI-powered features with careful attention to model lifecycle, evaluation pipelines, and responsible AI considerations alongside standard engineering risks. - Make sound technical and deployment decisions with appropriate risk mitigation and clear communication of trade-offs. - Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. - 8+ years of experience in software development, with demonstrated impact on large-scale, complex systems. - 2+ years of experience in technical leadership roles, driving architectural decisions and setting technical standards. - Proven track record of designing and delivering large-scale, complex software projects in SaaS, enterprise, or highly available production systems. - Deep expertise in one or more programming languages such as C#, .NET, Python, Java, or JavaScript. - Extensive experience with software architecture, design patterns, and system design at scale. - Strong understanding of cloud technologies and DevOps principles, with hands-on experience across build, test, and deployment pipelines. - Demonstrated expertise in building and operating AI-enabled systems safely, transparently, and compliantly. - Excellent problem-solving skills, attention to detail, and ability to navigate complex technical trade-offs. - Excellent communication and influence skills, with a demonstrated ability to drive technical excellence and organizational change through expertise and collaboration. - Master's degree or PhD in Computer Science, Engineering, or a related technical field. - Hands-on experience with LLM APIs (Gemini, OpenAI, Anthropic) and agent frameworks (Google ADK, LangChain, or similar). - Deep expertise in AI evaluation, observability, and testing strategies for non-deterministic systems. - Proven experience designing and implementing RAG patterns, prompt engineering strategies, and agentic workflow architectures. - Production experience with cloud platforms like Azure, AWS, or GCP, including infrastructure-as-code and cost optimization. - Advanced experience designing and implementing CI/CD pipelines and automation tools at scale. - Experience with observability platforms and monitoring complex AI/ML systems in production. - Track record of technical innovation, open-source contributions, or published work in AI/ML domains. - Experience operating effectively in complex, matrixed organizations with strong cross-functional influence. - Demonstrated commitment to responsible AI, diversity, inclusion, and accessible technology development.