Artificial Intelligence Engineer

We are looking for experienced, highly disciplined software engineers who are passionate about redefining how software is built through AI, automation, and Spec-Driven Development (SDD)., Senior Agentic Software Engineers design and deliver intelligent systems that combine AI agents, cloud-native architectures, automation frameworks, and modern software engineering practices. This role requires proven industry experience delivering production software, a strong foundation in software engineering fundamentals, and the discipline to apply those fundamentals rigorously when working alongside AI coding agents., This is a fast-paced role with tight deadlines and priorities that can shift quickly. We need someone who thrives in this type of environment: an engineer who can pivot without losing momentum, re-plan quickly when direction changes, and keep delivering high-quality work under time pressure., * 5+ years of professional industry experience designing, building, and delivering enterprise software solutions to production. \n

• Proven track record of delivering in fast-paced environments with tight deadlines and shifting priorities. \n

• Demonstrated ability to work independently with minimal supervision and own outcomes end to end. \n

• Portfolio of work (code samples, repositories, or project examples) preferred. \n, * Expert-level command of software engineering fundamentals: data structures, algorithms, design patterns, SOLID principles, clean code, testing strategies, and refactoring. \n

• Strong proficiency in one or more modern programming languages such as C#, Java, Python, TypeScript, or Go. \n

• Deep understanding of API design and distributed systems., * Strong experience with automated testing, including unit, integration, and contract testing, and with TDD practices., * Proven experience working directly with product owners to provide estimates, feasibility assessments, and technical tradeoff recommendations.

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• Design and implement complex agentic AI solutions using modern LLMs, AI agents, MCP integrations, orchestration frameworks, and cloud-native services. \n

• Build production-ready AI-enabled applications on AWS and Azure, including agent runtimes such as AWS AgentCore. \n

• Develop reusable frameworks, accelerators, templates, and engineering patterns that increase delivery capacity across teams. \n

• Design systems that balance autonomous agent behavior with human oversight, governance, and security controls. \n

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Software Engineering Fundamentals & Discipline

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• Apply strong engineering discipline to every deliverable: clean architecture, sound design patterns, meaningful test coverage, clear documentation, and maintainable code. \n

• Hold AI-assisted and agent-generated work to the same standards as human-written code. Speed from AI tools never replaces engineering rigor. \n

• Practice TDD, refactoring, and continuous integration as everyday habits rather than optional steps. \n

• Debug methodically, identify root causes, and build solutions that prevent recurring problems. \n

• Proactively manage technical debt and protect the long-term health of the codebase. \n

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Code Review (Including Agent-Generated Code)

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• Perform thorough, high-quality code reviews for both human-written and agent-generated code. \n

• Critically evaluate AI-generated code for correctness, security, performance, maintainability, test quality, and alignment with the specification and architecture. \n

• Identify subtle defects common in agent output, such as hallucinated APIs, incomplete error handling, insecure patterns, over-engineering, duplicated logic, and tests that pass without validating real behavior. \n

• Set and model review standards for the team and coach others on how to review agent-generated code effectively. \n

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Spec-Driven Development (SDD)

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• Lead software delivery using Spec-Driven Development methodologies. \n

• Translate business goals and user needs into clear, structured specifications that drive AI-assisted and agentic development workflows. \n

• Review and refine specifications to make sure they are complete, testable, and implementation-ready before work begins. \n

• Continuously improve SDD practices, tooling, templates, and engineering standards. \n

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Product Partnership, Estimation & Feasibility

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• Partner closely with product owners to shape work, providing realistic estimates and candid feasibility feedback early in the process. \n

• Identify technical risks, dependencies, and constraints, and propose options and tradeoffs that help product make informed decisions. \n

• Break complex ideas into right-sized user stories and deliverable increments. \n

• Re-estimate and re-plan quickly when priorities or scope change. \n

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Platform & Integration Engineering

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• Design and implement APIs, MCP servers, integrations, and service interfaces that give AI agents secure access to tools. \n

• Build event-driven and service-oriented architectures. \n

• Integrate enterprise systems, knowledge repositories, APIs, and business workflows into agentic ecosystems. \n

• Contribute to enterprise AI platform evolution and architecture standards. \n

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Mentoring & Upskilling

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• Mentor junior engineers through pairing, code reviews, design discussions, and hands-on coaching. \n

• Help upskill the broader team in SDD, agentic engineering, AI-assisted development, and core software engineering fundamentals. \n

• Share knowledge through demos, documentation, brown bags, and communities of practice. \n

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Communication

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• Communicate clearly and concisely with engineers, product owners, architects, and leadership, both in writing and verbally. \n

• Provide proactive, transparent status updates, including risks, blockers, and changes in direction. \n

• Explain complex technical concepts and tradeoffs in plain language for non-technical audiences. \n

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\n, * Extensive code review experience with a demonstrated ability to give clear, actionable, constructive feedback. \n

• Hands-on experience reviewing and validating code produced by AI coding agents and AI-assisted tools such as GitHub Copilot, Claude Code, Kiro, OpenSpec, or similar platforms. \n

• Hands-on experience with Spec-Driven Development concepts and workflows. \n

• Ability to break down complex business problems into structured specifications and implementation plans. \n

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Cloud & Agentic Development

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• Experience building production solutions on AWS and/or Azure. \n

• Understanding of AI agent architectures, orchestration frameworks, MCP, tool integration patterns, and human-in-the-loop workflows. \n

• Experience with event-driven architectures, serverless computing, containers, CI/CD pipelines, and Infrastructure as Code. \n

• Ability to set up CI/CD for new products without guidance. \n

• Familiarity with observability, telemetry, and operational monitoring. \n

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Product Collaboration, * Strong understanding of security principles, including authentication, authorization, encryption, and secure software development. \n

• Knowledge of common attack vectors (such as SQL injection, DoS, and XSS) and how to mitigate them, with particular attention to vulnerabilities introduced by AI-generated code. \n

• Ability to design AI-enabled solutions that meet governance, compliance, and risk management requirements. \n

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\n, * Experience building or operating solutions using AWS AgentCore (strongly preferred). \n

• Experience with Strands, LangChain, Semantic Kernel, AutoGen, CrewAI, or similar orchestration frameworks. \n

• Experience implementing MCP servers and tool ecosystems. \n

• Experience designing AI agents that interact with enterprise systems and business workflows. \n

• Experience creating engineering standards, reusable frameworks, or internal developer platforms. \n

• Experience evaluating emerging AI technologies and turning them into practical enterprise solutions. \n

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