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Tech Stack
Tools & technologiesAWSCloudDistributed SystemsGoogle Cloud PlatformKubernetesMicroservicesPython
About the role
Key responsibilities & impact- Own the technical architecture and engineering strategy for AutoQuote's cloud-native platform — spanning AI feature integration, data pipeline infrastructure, microservices design, and platform reliability.
- Lead the design and delivery of the highest-complexity, highest-impact engineering initiatives on the team, setting the architectural patterns and engineering standards that guide the broader organization.
- Define and enforce software quality standards, AI usage guidelines, and engineering best practices across the AutoQuote engineering organization.
- Partner with engineering leadership, product management, and program stakeholders to translate program strategy into a coherent technical roadmap and prioritized backlog.
- Evaluate, prototype, and champion emerging AI capabilities — including LLM integration, agentic frameworks, and AI-assisted development tooling — driving adoption across the team and into the product.
- Drive responsible AI governance across AutoQuote, including prompt engineering standards, AI output evaluation practices, and compliance with Cisco security and data handling policies.
- Mentor and technically develop senior and mid-level engineers; serve as the primary technical authority and critical issue point for complex engineering decisions.
- Lead architecture reviews, critical design decisions, and cross-functional technical alignment sessions.
- Represent AutoQuote engineering in program-level and executive forums; communicate technical tradeoffs, risks, and decisions with clarity to both technical and non-technical audiences.
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field; advanced degree preferred.
- 8+ years of professional software engineering experience, with a demonstrated track record at the principal, staff, or distinguished engineer level.
- Deep proficiency in Python; demonstrated ability to architect polyglot, cloud-native solutions — technology selection should always be driven by what is right for the problem, not a single prescribed stack.
- Proven expertise in cloud-native architecture on GCP and/or AWS, including Kubernetes, distributed systems, managed AI/ML services, and data pipeline infrastructure.
- Extensive hands-on experience applying AI/ML services and frameworks (LLM APIs, LangChain, cloud-managed AI services) in production engineering environments.
- Demonstrated mastery of LLMs as engineering tools — prompt engineering, AI-assisted development, model output evaluation, and designing AI-powered workflows and agentic systems.
- Track record of setting technical standards, driving architecture decisions at a program or organization level, and leading cross-functional engineering initiatives.
- Strong technical communication skills — able to write crisp technical proposals, represent engineering tradeoffs to executive stakeholders, and influence without direct authority.
- Experience operating and improving high-availability production systems at enterprise scale.
Benefits
Comp & perks- All work is conducted in alignment to Cisco security policy and compliance requirements, including responsible handling of data and AI-generated content.
ATS Keywords
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Hard Skills & Tools
Cloud-Native SolutionsKubernetesDistributed SystemsData Pipeline InfrastructureLLM IntegrationPrompt EngineeringAI-Assisted DevelopmentModel Output EvaluationPolyglot ArchitectureHigh-Availability Production Systems
Soft Skills
MentoringTechnical AuthorityInfluencing Without Authority
Certifications
Bachelor's Degree in Computer ScienceMaster's Degree in Software Engineering