Sr Software Engineer - AI

McGraw Hill LLC.

11h•Remote

About The Position

Imagine building AI that doesn't just generate text — it teaches. At McGraw Hill, our AI Platform team is creating intelligent learning experiences used by millions of students and educators worldwide. We're not bolting AI onto legacy products. We're rethinking how people learn by putting generative AI, retrieval-augmented generation, and agentic workflows at the center of the experience — and shipping it at enterprise scale. This is applied AI with real stakes: a tutor that adapts in real time, content generation that meets rigorous academic standards, and intelligent tools that give teachers hours back in their week. The models are powerful. The problems are genuinely hard. And the impact — helping a student finally grasp a concept they've been struggling with — is something you'll actually feel. Your impact on team We're looking for a Senior Software Engineer — AI to join our AI Platform team. You'll own the design, development, and delivery of AI-powered products end-to-end — from prototyping new capabilities with LLMs to hardening them for millions of concurrent users. You'll work primarily on our backend systems — Python (FastAPI) and Go (Gin) microservices, RAG pipelines, and LLM orchestration — collaborating closely with data scientists, product managers, and designers to turn research into shipped product. This isn't a role where you follow a spec. You'll operate with significant autonomy, navigate ambiguity, and drive technical direction for your projects. You'll mentor engineers on the team, raise the quality bar through design reviews and code review, and help establish the patterns and practices that define how we build AI at McGraw Hill. This is a remote position open to applicants authorized to work for any employer within the United States.

Requirements

Experience building production software. 5+ years of professional software development experience, with a strong track record of delivering and operating distributed systems. You've shipped features end-to-end and owned services in production.
Hands-on AI engineering skills. Practical experience building generative AI applications — working with LLMs (Azure OpenAI or similar), prompt engineering, RAG architectures (vector databases like Pinecone, embedding models, semantic chunking and routing), and custom orchestration pipelines. You understand how to evaluate model outputs, optimize retrieval quality, and build reliable AI-powered user experiences.
Backend-strong, polyglot capable. Deep proficiency in Python (FastAPI, async/await, Pydantic) and working knowledge of Go (or willingness to become proficient quickly). Experience building and maintaining microservices, async task workers (Celery/SQS), and APIs that power AI capabilities. Comfortable with PostgreSQL (including Aurora and read/write splitting patterns). Able to work in modern frontend frameworks (React or Angular) when needed, with support from the team — but your home base is the backend.
Cloud and infrastructure fluency. Production experience with AWS (ECR, RDS Aurora, S3, SQS, IAM) and Azure (Azure OpenAI, private endpoints). Hands-on with Terraform for infrastructure-as-code and Kubernetes (Kustomize, HPA) for container orchestration.
An observability and quality mindset. Experience with APM and monitoring tools (New Relic, Datadog, or similar). You understand structured logging, distributed tracing, and metrics collection. You value code quality — static analysis, type checking, security linting, and comprehensive test suites (unit, integration, performance).
An ownership mindset. You don't wait for someone to scope your work. You identify problems, propose solutions, and drive them forward. You manage ambiguity well and proactively raise risks when you see them.
Strong communication and collaboration skills. You write clear design docs and API documentation, give constructive code reviews, and explain complex technical concepts to non-technical partners. You build trust through transparency and follow-through.
Passion for the mission. You're excited about the intersection of AI and education — and motivated by the idea that the systems you build will directly help students learn.

Nice To Haves

Familiarity with semantic routing and semantic chunking libraries for RAG pipelines
Experience with k6 or similar performance/load testing frameworks
Background in edtech, adaptive learning, or content-rich product domains
Experience with evaluation frameworks for LLM outputs (automated scoring, human-in-the-loop review)
Familiarity with agentic AI patterns and multi-step reasoning workflows
Experience implementing accessible features in web applications (WCAG 2.2 AA)
Familiarity with secrets management patterns (SOPS, AWS KMS)

Responsibilities

Build and ship AI-powered products: Design, develop, and maintain generative AI applications — from RAG pipelines with vector search and semantic chunking to LLM orchestration, production APIs, and async task workflows. You'll own features from concept through deployment and monitoring, making pragmatic architectural decisions that balance innovation with reliability at scale.
Operate a dual-language backend: Work across Python (FastAPI) and Go (Gin) microservices, understanding when each language's strengths apply. Build async-first APIs, background workers (Celery/SQS), and data pipelines that handle millions of concurrent users.
Own infrastructure and observability: Contribute to Terraform modules, Kubernetes manifests (Kustomize), and CI/CD pipelines (GitHub Actions). Instrument services with our observability stack (New Relic, Datadog, Prometheus) so the team can ship with confidence and debug production issues quickly.
Lead through technical excellence: Drive design reviews, write clear technical documentation, and make thoughtful decisions about tradeoffs. Improve team standards around code quality, testing, observability, and system reliability. You're the engineer others come to when they're stuck on a hard problem.
Mentor and multiply: Support the growth of engineers on the team through pairing, code review, and design guidance. Share knowledge proactively and foster an inclusive, learning-oriented team culture.
Collaborate cross-functionally: Partner with data scientists to evaluate model performance and fine-tune prompts. Work with product managers and designers to translate product vision into technically sound solutions. Communicate tradeoffs clearly and build trust across teams.
Stay at the frontier: The AI landscape moves fast. You'll experiment with new models, frameworks, and techniques — and bring a point of view on which advancements are worth integrating into our platform and which are noise.
Ship with confidence: Build with accessibility in mind, meeting WCAG 2.2 AA standards. Champion code quality through static analysis, type checking, security linting, and comprehensive test suites.

Benefits

We offer clear career paths, competitive compensation, and the rare opportunity to work at the intersection of two of the most exciting fields in technology: applied AI and education.
An annual bonus plan may be provided as part of the compensation package, in addition to a full range of medical and/or other benefits, depending on the position offered.

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