Principal Engineering Lead

Amira Learning is a leader in AI-driven educational technology, focused on enhancing literacy outcomes. They are seeking a Principal Engineer Lead to drive the design and development of their AI-based educational products, taking ownership of complex systems and leading a high-velocity team in building robust backend services and data pipelines.

Responsibilities:

• Design and build robust, secure, and scalable backend services for reporting, analytics, and data delivery
• Work across Node.js, Python, .NET, and SQL, with data pipelines that support school districts and internal stakeholders
• Operate within a modern AWS environment using services like Lambda, Athena, Glue, DynamoDB, Redshift, and S3
• Participate in reviews, propose architecture improvements, and drive system evolution. Proactively identify technical debt, performance bottlenecks, and high-leverage opportunities—don’t wait to be asked
• Use AI tools (Claude, Cursor, etc.) as primary development instruments—treating prompt crafting, context management, and output validation as core engineering skills, not supplements
• Rapidly prototype and iterate on backend services, APIs, and data pipelines using AI-assisted development—focusing on clear intent, fast feedback loops, and shipping over perfection in early iterations
• Build and refine repeatable workflows where AI handles implementation and the engineer focuses on architecture, review, edge cases, and integration
• Apply strong judgment about when AI-generated code is trustworthy and when deeper review or manual implementation is warranted—especially for data pipelines serving school districts where correctness matters
• Define architectural patterns and guardrails for integrating AI into backend systems, including prompt management, cost controls, and evaluation frameworks
• Lead by example in AI-native development—demonstrating how to move from idea to working system rapidly, how to evaluate AI-generated code critically, and when to go deep manually versus staying at the intent layer
• Coach and mentor engineers in AI-first workflows, helping them shift from 'write everything' to 'direct, validate, and iterate'—building team comfort and fluency with prompt-driven development
• Establish team norms around AI-assisted work: when to trust AI output, when to inspect closely, how to structure prompts for complex systems work, and how to maintain code quality when generation is fast
• Design and maintain shared AI resources—prompt libraries, Claude Projects, reusable workflows—that raise the floor for the entire team
• Integrate AI into deployment, code review, and testing processes to accelerate team throughput while maintaining quality
• Collaborate with product owners, engineers, data scientists, and customer teams to define and deliver high-impact solutions
• Translate between product, business, and engineering by distilling strategy into actionable technical direction and clearly communicating risks, tradeoffs, and constraints
• Own and continuously improve team delivery flow, including managing WIP, ensuring work reaches a shippable state, and proactively addressing bottlenecks that impact predictability and quality
• Measure and improve team velocity through AI adoption—tracking how AI-assisted workflows change throughput, defect rates, and time-to-ship

Requirements:

• Strong background in cloud-based architectures, APIs, and modern software development practices, including CI/CD pipelines and containerized environments
• Deep understanding of AWS services, including but not limited to Lambda, DynamoDB, API Gateway, S3, and CloudFormation
• Demonstrated experience working in an AI-first development style—using LLM-based tools as a primary means of building, not just an occasional assist
• Hands-on experience with AI coding tools (Claude, Cursor, or similar) in day-to-day development workflows
• Strong judgment about when AI-generated code is production-ready versus when it needs manual refinement—particularly in data-critical contexts
• Experience introducing AI-assisted workflows to a team and shifting development culture toward prompt-driven practices
• Comfort evaluating build-vs-buy decisions for AI capabilities, including managed services (e.g., AWS Bedrock, SageMaker) versus custom implementations
• Passionate about education and improving learning through technology
• A self-starter who takes ownership of outcomes—not just tasks—and thrives on shipping high-impact features, learning from user feedback, and proactively identifying the next problem to solve
• Believes the future of software engineering is about directing systems, not just writing them—and is excited to figure out what that looks like in practice for a small, high-performing team
• Comfortable with startup pace—tight deadlines, shifting priorities, and building elegant solutions with imperfect information. You ship, learn, and iterate
• Strong collaboration skills, with the ability to work cross-functionally with other engineers, architects, and stakeholders outside of R&D such as Customer Success and Support
• Clear and effective communicator who can articulate technical ideas to a diverse audience
• Bachelor's degree in Computer Science, Engineering, or related field preferred—but we value demonstrated ability and a strong portfolio over credentials
• 8+ years of backend development experience, with a strong background in building and maintaining production systems
• Experience working in a startup or early-stage environment where you owned large surface areas with minimal structure
• Skilled in Node.js, Python, and/or .NET and cloud-first application design
• Experience working with relational and NoSQL databases, distributed data pipelines, and large-scale data processing
• Familiarity with PySpark, Glue, or similar frameworks is a plus
• Deep knowledge of AWS or similar cloud platforms, and comfort working in serverless environments
• Track record of identifying high-leverage opportunities to apply AI to real problems—not just internal tooling, but product and data capabilities
• Experience with AI/ML infrastructure concepts: model serving, RAG architectures, evaluation pipelines, or vector databases
• Strong communicator who thrives in a collaborative, fully remote team environment
• Self-directed and able to work autonomously with minimal supervision while maintaining productivity
• Comfortable acting as a technical lead—driving implementation forward, making architecture decisions without escalation, and unblocking peers—while staying hands-on in a fully remote, async-first environment
• Mission-aligned and energized by helping students succeed