AI Engineer

We are looking for an AI Engineer to design, build, and ship AI models and AI-native features that power the next wave of learning at

Analytics Vidhya — adaptive curricula, intelligent tutors, automated assessments, and content generation. You will own ideas end-to-end

and use AI-powered dev tools like Claude Code and Codex to ship faster than a traditional ML team.

• 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.

• 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.

• 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

.

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