AI Engineer

ResponsibilitiesDesign, build, and operate production-grade software systems that embed AI as a core capability rather than an add-on.Build and integrate agentic systems that can autonomously select tools, manage context, and execute multi-step tasks toward defined goals.Implement Model Context Protocols (MCPs) or equivalent mechanisms to manage context, tools, memory, and system boundaries in production environments.Translate product requirements into AI-enabled implementations that are scalable, reliable, secure, and observable.Apply AI through prompting, orchestration, model composition, evaluation, and deployment, focusing on runtime behavior rather than training.Treat model inputs, outputs, and behavior as versioned, testable components of the system.AI-First Software DevelopmentUse AI directly in daily engineering work, including:AI-assisted and AI-generated codeAutonomous and semi-autonomous engineering agentsAI-driven test generation, debugging, and refactoringAI-enabled documentation and observabilityTreat AI as a default everyday tool to increase personal and team leverage.Iterate on prompts, policies, agent flows, and contextual inputs as part of normal development workflows to improve system behavior and outcomes.Apply and refine AI-enabled development practices within assigned systems and team workflows to reduce manual effort and improve delivery quality.Product & Platform CollaborationPartner closely with Product, Scrum Masters, and other engineers to deliver AI-powered features end to end. Take leadership of these roles at times, including acting as a single person team when appropriate for the project. This is true full-stack in an AI-powered environment.Integrate AI systems with real-world services, APIs, and data sources as part of production applications.Contribute to shared platforms, services, and internal tooling by implementing AI enabled solutions aligned with established patterns and architectural direction.Balance rapid iteration with sound engineering judgment, system boundaries, and long term maintainability.Quality, Reliability & AccountabilityOwn the quality of your deliverables top to bottom of the stack. Partner with QA team members when available, but build AI agents focused on guaranteeing quality of what you build without the need of others.Ensure AI-enabled systems meet enterprise standards for security, performance, reliability, and compliance.Implement safeguards, monitoring, and evaluation mechanisms for AI behavior in production, including detection of hallucinations, regressions, and unintended behavior.Treat evaluation, traceability, and explainability as ongoing engineering concerns rather than one-time validation steps.Take ownership of assigned solutions from initial design through deployment and ongoing operation.Continuous Improvement & LearningStay current on AI tools, platforms, models, and agentic patterns relevant to real-world software systems.Experiment responsibly with new AI capabilities and translate learnings into concrete improvements in the systems and workflows you own.Continuously improve how AI is applied within your area of responsibility through disciplined execution and iterative refinement.Contribute to a culture of technical excellence, accountability, and continuous learning through high-quality delivery.Other duties as assigned.QualificationsBachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.Strong software engineering fundamentals, with experience building and operating production systems.Hands-on experience building or working with real agentic AI systems, including giving models autonomy to select tools and execute multi-step tasks.Experience using Model Context Protocols (MCPs) or equivalent approaches to manage tools, context, and memory in production AI systems.Demonstrated technical understanding of AI models, including:Model names and versionsParameter sizes and context window limitsSystem prompts vs. user promptsTrade-offs between models (latency, cost, reasoning quality)Experience working with open-source AI models and ecosystems, in addition to any commercial or hosted models.Experience integrating AI systems with real-world services, APIs, and data sources.Demonstrated ability to evaluate AI systems beyond accuracy, including reliability, hallucination risk, traceability, and safety considerations.Experience addressing scaling concerns such as latency, cost optimization, caching, parallelization, and trade-offs between retrieval-based approaches and fine-tuning.Demonstrated ability to move AI systems from prototype to pilot to production, including awareness of common failure modes and how to mitigate them.Comfort working autonomously in small, highly empowered teams.Preferred SkillsExperience integrating frontier and open-source models into SaaS or enterprise applications.Familiarity with cloud-native architectures and modern development stacks.Experience working in regulated, enterprise, or mission-critical environments.Background in product-oriented engineering organizations.