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Partner with business stakeholders to translate product vision into technical and data requirements for AI-powered solutions, advising on what's achievable, what's risky, and what requires further investigation.
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Own LLM application engineering (prompting + RAG + tool use + evaluation + guardrails + orchestration) as a core technical discipline, driving iterative optimization in partnership with subject matter experts.
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Define and oversee evaluation frameworks for AI-powered features, coordinating with subject matter experts to ensure inference quality, safety, and alignment with pedagogical standards
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Evolve production monitoring and regression testing for inference quality, cost, and latency, driving iterative improvements post-release.
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Evaluate emerging technologies and drive adoption of best-in-class tools and frameworks, incorporating their capabilities into the platform.
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Lead a globally distributed team and drive sound engineering design, implementation, quality, deployment and monitoring practices on AI-powered microservices and products.
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Architect distributed systems to ensure high availability, low latency, and fault tolerance.
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Leverage AWS services such as Bedrock, Lambda, ECS, DynamoDB, RDS, and S3 to build cloud-native solutions.
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Enforce security best practices across the codebase and AWS infrastructure, implementing defense-in-depth strategies and driving timely risk mitigation and remediation of vulnerabilities.
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Drive code automation practices across the team to ensure maintainability and extensibility
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Maintain hands-on involvement through prototyping, POCs, and direct contribution to critical implementations.
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Provide technical mentorship to engineering teams and conduct code and architecture reviews.
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Contribute to the organization's AI Center of Excellence by developing reference implementations, documentation, and best practices, while tracking the evolving AI landscape and identifying the right moments to introduce new capabilities.
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Collaborate with cross-functional teams, including frontend engineers, product managers, and operations teams, to align on technical solutions.
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Software Design, Implementation (i.e., coding), and Testing.
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Solid understanding of ML/AI data practices: dataset curation, validation, and evaluation design.
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Experience with agent architectures, RAG, and tool-use patterns in production LLM applications is a plus.
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Proficient in database development in relational and NoSQL, Postgres and Amazon DynamoDB preferred.
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Understanding of Cloud Architecture and experience working with AWS resources, including cloud networking and security fundamentals.
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Strong, creative problem-solving and troubleshooting skills are a must, as is the ability to coach junior team members in these areas with proven ability to drive adoption of new processes and tools.
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Must assimilate information, distill knowledge, apply experience, and provide solution alternatives and recommendations.
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Must have strong time management skills - including the ability to work well under pressure, plan, set priorities across multiple projects, adapt to change, and meet established timelines.
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Effective written and verbal communication skills.
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AWS certification is a plus (Cloud Practitioner or higher)
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Experience in education technology or other high-stakes domains is a plus.