AI Engineer
Core
Design, build, evaluate, and produce AI-powered solutions using LLMs, retrieval systems, and classical ML for enterprise and product use cases in education technology.
Role type
Applied AI Engineer (LLMs, RAG, ML pipelines)
Builds
AI-powered applications, APIs, and automation pipelines for learners, partners, and exam operations
Domain
Education technology / Assessment and certification
Deliverable
production ML models | product features | infrastructure
Required skills
Python, PyTorch, TensorFlow, Hugging Face, scikit-learn, FastAPI, Streamlit, foundation models, LLMs, transformers, embeddings, RAG, multimodal AI, cloud AI platforms (Azure OpenAI, Azure AI Search, Azure ML, Azure Cognitive Services)
Preferred skills
API design best practices, retrieval-augmented generation, hybrid search, semantic ranking, vector databases, cloud deployment, CI/CD, Docker, Kubernetes, serverless architectures, prompt engineering, LLM evaluation techniques
Technologies
Azure OpenAI, Azure AI Search, Azure ML, Azure Cognitive Services, PyTorch, TensorFlow, Hugging Face, FastAPI, Streamlit
Responsibilities
Design, develop, fine-tune, and evaluate AI, ML, and generative AI models; Build AI-powered applications, APIs, and automation pipelines; Develop experimentation and evaluation workflows for prompts, models, retrieval quality, and benchmarking; Collaborate with Data Engineers and business stakeholders to prepare, validate, and manage high-quality datasets; Work with MLOps / platform teams to deploy, monitor, and maintain AI solutions in production
Seniority
Mid-level, hands-on IC