AI Engineer
Analytics Vidhya
Full Time2+ yearsPosted 8 days ago
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Overview
Position Type
Full Time
Experience
2+ years
Job Description
WHAT YOU 'LL DO
- Build AI models — fine-tune and deploy LLMs, embedding models, and classical ML to power tutoring, recommendations, assessments, and content generation across our Ed-Tech products.
- Prototype to production — translate fuzzy product ideas into working prototypes in days, then harden them into reliable services with proper evals, guardrails, and monitoring.
- AI-native engineering — use Claude Code, Codex, and Cursor as daily drivers to design, refactor, and test code; orchestrate agents and tool-use for non-trivial workflows.
- Retrieval & agents — design RAG pipelines, vector search, and agentic systems over our learning content, course catalogs, and user signals.
- Evals & quality — build evaluation harnesses, golden sets, and A/B experiments to keep model quality measurable and improving.
- Collaborate cross-functionally — partner with product, design, and content teams to scope problems, define success metrics, and ship learner-facing impact.
MUST HAVE
- 2–6 years building and shipping AI / ML systems in production (not just notebooks).
- Hands-on fluency with AI-powered dev tools — Claude Code, Codex, Cursor — and the judgment to know when to lean on them vs. write it yourself.
- Strong Python; solid grasp of modern LLM stacks (OpenAI, Anthropic, Hugging Face, LangChain / LlamaIndex or equivalents).
- Experience with prompt engineering, fine-tuning, RAG, embeddings, and at least one vector DB (pgvector, Pinecone, Weaviate, Qdrant).
- Comfort with cloud (AWS / GCP / Azure), Docker, and basic MLOps — model serving, versioning, observability.
- Bachelor's in CS, ML, or related field; bias for shipping, strong written communication, and clear thinking under ambiguity.
BONUS POINTS
- Built or shipped AI features in Ed-Tech, data science, or analytics platforms.
- Experience with multi-agent systems, MCP, tool-use, or evaluation frameworks (e.g., Inspect, Promptfoo, Ragas).
- Open-source contributions, public AI projects, or technical writing on LLMs and applied ML
WHY JOIN US
Shape AI products used by millions of learners · high ownership with product & leadership · fast, learning-driven culture · competitive compensation and growth. Apply now and help build the future of AI-native learning