Assistant Llm Developer Jobs in Ohio (NOW HIRING)

Overview:
Software Engineer II -AI Platform {Teacher Assistant)
Remote
Impact the Moment
At client , our AI Platform team is building intelligent learning experiences used by millions of students and educators. Teacher Assistant is a direct-to-teacher AI tool - an agentic chatbot, embedded in the learning platform teachers already use, that helps K-12 teachers plan instruction, find and use their course materials, and make sense of student performance data through natural conversation. This is applied AI with real stakes. T he orchestration is genuinely hard, and the impact - helping a teacher reach a student who's been struggling - is something you'll actually feel.
About this engagement
You'll join a small, senior delivery team building and operating the Teacher Assistant backend and the AI platform around it. We're looking for someone who can pick up well-scoped features and own them through to production with light support - someone who has shipped production AI systems before and understands how they behave once real users are in the loop. A note on shape: this is a full-stack-leaning-backend role with a strong AI-systems emphasis. It is not a pure machine-learning role and not a pure web role. Most of your time is in async Python and the LLM orchestration layer; you'll touch the frontend when the work calls for it.
What you'll do
Day to day, you'll:
• Build and extend the agentic LLM orchestration behind Teacher Assistant - the graph of nodes and agents that turns a teacher's request into a useful
response .
• Integrate external data sources and tools into the agent so it can reason over the information teachers need .
• Improve retrieval quality across vector and lexical search .
• Work on model routing, resilience, and graceful degradation so the system stays fast and available under real-world load.
• Strengthen the prompt lifecycle, evaluation discipline, and observability that keep a nondeterministic system reliable .
• Harden quality with automated unit, integration, and end-to-end testing
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This is a software engineering role on a productionAI system. You won't be training or fine-tuning models or running model-science experiments - our data science team owns that. But unlike a generic application role, promptengineering,retrievalquality,andevaluationdisciplinearecore tothisjob,not someone else's problem. The interesting work lives in the orchestration, the prompt lifecycle, retrieval, and evals - not CRUD.
What the role looks like at this level
As a Software Engineer II on this team, you'll break down medium-sized features, estimate them, and cut scope to ship on time. You'll start to own tasks within the service with support from senior teammates, contribute to technical design and engineering-review proposals while thinking through failure cases, give helpful and timely code reviews, and defend your decisions in review. You'll debug to root cause in your area, instrument your code for operations, and participate in the on-call rotation. Senior engineers are around to pair with and review your work - but increasingly you'll be the one proposing the approach and carrying a feature to production.
What you must already bring
You don't need every line below at expert depth, but the combined surface has to be covered.
Core engineering
. Expert-levelasyncPython (3.11+). Real production asyncio / async / await experience across the request path - a synchronous-only Python background won't be enough here.

• FastAPI at depth: routers, dependencies, lifespan, middleware. Pydanticv2and disciplined type hints.
• pytestand pytest-asyncio- fixtures, async, mocking, and meaningful coverage. Standard formatting, linting, and type-checking tools are table stakes.

AI/ LLM systems - theheart of the work

• Hands-on production experience with LangGraph:state machines, conditional edges, checkpointing. Experience with LangChain alone is not the same thing - this is where most of the surface area lives.
• LangChaincore(messages, runnables, tools), and promptengineering/promptlifecyclemanagement- versioned, environment-tagged prompts with local overrides - using tracing and experiment tooling such as LangSmith.
• Multi-agent/ multi-node workflow design- routing across specialized agents and nodes.

• RAGwith hybrid vector + lexical retrieval, and experience with a managed LLM provider such as Azure OpenAI(deployments, API versions, quotas).
• Sound instincts for non-determinism,token budgets, timeouts, and graceful degradation,

plus familiarity with eval frameworks (e.g. LLM-as-judge and regression evals).
Data, infrastructure, and delivery
. PostgreSQL operationally - indexing, connection pools, poolers - plus pgvector and
OpenSearch/Elasticsearch hybrid (text + KNN) search.
. AWS and Kubernetesin production - genuine fluency, beyond local container orchestration. Dockermulti-stage builds; infrastructure-as-code (e.g. Terraform)and manifest overlays for multiple environments.
. Multi-environmentconfigurationdiscipline - several environments, from local through production, each with its own secrets, prompts, and resources.
And comfortable with

• Typescriptand modern Angularwith RxJSwhen frontend work is needed. A backend-leaning candidate is welcome as long as you're comfortable in Angular; a frontend-leaning candidate must still be solid in the Python/LLM stack.

Nice to have {genuine bonuses, none required)

• MCP (Model Context Protocol) and SSE; database migration tooling; Redis-compatible caches.
• Observability tooling (APM, metrics, tracing) and distributed-tracing concepts.
• Modern Python packaging and build tooling, Make-based builds, GitHub Actions, private package registries, and encrypted-secrets workflows.
• Load testing and end-to-end browser testing frameworks.
• Edtech / K-12 domain awareness (standards, proficiency, learning frameworks) and FERPA-adjacent data-privacy thinking.
• Familiarity with large-enterprise internal identity, auth, and content-metadata services - accelerates ramp, but learnable.

How we work
This is an internal enterprise codebase, so expect internal SDKs and package registries, encrypted-secrets tooling, and a secrets manager as part of the daily flow. It's a polyglot repo - backend, frontend, infrastructure-as-code, and database migrations coexist - and the team uses written design and decision docs. Security hygiene for AI apps (prompt injection, PII handling, guardrails) matters here because we're working with educational data.
Skills:
Artificial Intelligence,fullstack