Per Scholas is looking to solve the problem of driving mobility and opportunity in the ever-advancing technology landscape by unlocking the untapped potential of individuals, uplifting communities, and meeting the needs of employers through rigorous tech training.
Requirements
- Build end-to-end AI applications that compose LLM prompting, Retrieval-Augmented Generation (RAG), agentic tool/function calling, multimodality (OCR to text), and basic evaluation/guardrails for production-minded delivery.
- Orchestrate LLM workflows with function/tool schemas, conversational memory, validation, moderation, and retry/timeout patterns; analyze traces and metrics to improve reliability.
- Design and tune retrieval pipelines: ingest, chunk, embed, index, and retrieve with citations; optimize chunk size, overlap, and top-k for accuracy; measure grounding and hallucination rates.
- Python 3.10+ and ecosystem: LangChain, vector stores (Chroma; Pinecone preferred), FastAPI/Streamlit, pytest, Git/GitHub; model SDKs (OpenAI, Anthropic); OCR with Tesseract; local runtimes (e.g., Ollama).
- Data modeling and warehousing: Design star and snowflake schemas, conformed dimensions, SCD types, surrogate keys; apply Kimball-style dimensional modeling for analytics.
- Power BI analytics engineering: prepare data with Power Query; model governed semantic layers with relationships and DAX; publish to Service with scheduled/incremental refresh and workspace management; secure access via Row-Level Security.
- SQL for reporting and transformation plus Excel for analysis; craft KPI-driven, stakeholder-ready dashboards and narratives aligned to PL-300 expectations.
Responsibilities
- Lead engaging, lab-first instruction on LLMs, prompt engineering, RAG, agentic AI, and multimodal apps; guide learners to build working CLI/API/UI deliverables.
- Teach analytics engineering: SQL for reporting, Power Query for shaping, semantic modeling and DAX, and deployment to Power BI Service with refresh and security.
- Mentor capstones and portfolios end-to-end, from scoping and data sourcing to evaluation, guardrails, observability, and stakeholder-ready demos.
- Serve as AI subject-matter expert: advise on curriculum, align content to market demand and certifications (e.g., PL-300), and track emerging tools and patterns.
- Coach fellow instructors on AI-native teaching practices and classroom use of copilots while modeling ethical and responsible AI use.
- Provide timely feedback, office hours, and career coaching that translate projects into employer-ready narratives.
- Collaborate with staff to continuously improve labs, rubrics, and outcomes; uphold inclusive, high-expectation learning environments and program policies.
Other
- 1–3+ years teaching, mentoring, or training in technical subjects.
- Ability to clearly communicate complex AI concepts to diverse learners.
- Experience guiding learners through portfolio projects or capstones.
- Bachelor’s degree in Computer Science, AI, Software Engineering, or equivalent experience.
- 3+ years of professional experience in AI/ML or software engineering.
