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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI solution architecture, including the design and implementation of production-grade APIs and backend services. Proficient in Python and cloud-native architectures, with a strong focus on data pipelines and system observability.
Highest-signal resume keywords
AI Solution ArchitectureProduction API DevelopmentPython ProficiencyCloud-Native ArchitecturesData Pipeline Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringBackend DevelopmentModel ServingSystem ObservabilityData Privacy PracticesExperimentation FrameworksA/B TestingAgile DevelopmentPrompt EngineeringML Model Operationalization
Soft Skills
CollaborationMentoringDecision-MakingProblem-SolvingCommunication
Tools & Technologies
AWSGCPAzureAPIsData AnalyticsFeature FlagsMonitoring ToolsLogging ToolsVersion Control SystemsDocumentation Tools
Industry Keywords
AI-Powered ProductsData-Driven ProductsCross-Functional TeamsTechnical LeadershipIterative Development
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformJavaJavaScriptNode.jsPythonGo
About the role
Key responsibilities & impact- Lead the technical design and implementation of AI-powered product features from concept through production.
- Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration.
- Make pragmatic decisions to accelerate delivery while maintaining system integrity.
- Ensure AI solutions are secure, observable, scalable, and aligned with platform standards.
- Act as the technical lead for a cross-functional AI Pod.
- Break down product requirements into executable technical workstreams and prototypes.
- Guide rapid iteration cycles, proofs-of-concept, and MVPs while balancing experimentation with production readiness.
- Review code, architecture, and technical decisions to maintain quality and velocity.
- Partner with Product Management to shape problem definitions, success metrics, and delivery plans.
- Collaborate with the Data Science Leader to integrate models, analytics, and data assets into product workflows.
- Translate data science outputs into consumable APIs, services, and product features.
- Provide technical feedback on feasibility, scope, and tradeoffs during product discovery.
- Ensure features are production-grade, including monitoring, logging, and performance tracking.
- Implement AI guardrails around reliability, latency, cost controls, and failure modes.
- Support experimentation frameworks, A/B testing, and post-launch learning loops.
- Drive responsible AI practices, including explainability, bias awareness, and data privacy considerations.
- Define and enforce lightweight engineering standards for AI-enabled systems.
- Promote reuse of components, prompts, pipelines, and services across AI initiatives.
- Mentor pod engineers on AI-adjacent system design and best practices.
- Contribute to internal documentation and shared AI patterns/playbooks.
Requirements
What you’ll need- BS or MS in Computer Science, Engineering, or related technical field.
- 5+ years of software engineering experience, including leading complex systems.
- Strong experience designing and building production APIs and backend services.
- Proficiency in Python and at least one backend language (e.g., Java, Node.js, Go).
- Experience with cloud-native architectures (AWS, GCP, or Azure).
- Solid understanding of data pipelines, model serving, and system observability.
- Ability to work closely with product teams in fast-moving, iterative environments.
- Comfortable working in an English-speaking professional environment.
- Preferred: Experience working in AI-first or data-driven product teams.
- Preferred: Familiarity with modern LLM platforms, prompt engineering, and agent frameworks.
- Preferred: Experience operationalizing ML models (model serving, monitoring, versioning).
- Preferred: Exposure to experimentation platforms, feature flags, and A/B testing.
- Preferred: Experience in Agile or product-led development environments.