AI Specialist II

About the role

Coursera is seeking an AI Specialist II to design, prototype, and ship applied generative AI solutions that power learning experiences. The role blends data exploration, AI system design, rapid experimentation, and rigorous evaluation to turn ambiguous business problems into measurable AI products.

Key responsibilities

• Build and design AI systems using retrieval pipelines, agentic workflows, and structured extraction from unstructured sources.
• Architect Retrieval Augmented Generation (RAG) pipelines with vector databases such as Pinecone or Weaviate.
• Develop and maintain agentic AI workflows with frameworks like Langgraph, Mastra, LlamaIndex, CrewAI, or AutoGen.
• Design and run comprehensive evaluation suites covering accuracy, hallucination, grounding, and human review.
• Work with large‑scale structured and unstructured datasets on cloud‑native storage (S3, Postgres, GCS, BigQuery, Databricks) using strong SQL and data‑modelling skills.
• Rapidly prototype AI solutions, experiment with models, prompts, and retrieval strategies, and iterate based on results.
• Partner with product managers, data engineers, and software engineers to translate business problems into scoped AI solutions with clear KPIs.
• Document architectures, design decisions, runbooks, prompts, evaluation results, and troubleshooting guides.

Required profile

• 4+ years of experience in Data Science, Applied AI, or Machine Learning, delivering data‑driven or AI‑powered solutions.
• Proven track record taking AI use cases from exploration through validated prototype to production.
• Hands‑on experience with modern Generative AI techniques such as LLMs, embeddings, RAG, semantic search, NLP, recommendation, or agentic systems.
• At least two production deployments of LLM‑based systems (fine‑tuning, RAG, agentic workflows, or prompt‑engineered solutions).
• Experience designing AI experiments, defining quality metrics, analyzing errors, and iterating on results.
• Proficiency in Python and SQL for data manipulation, analysis, and AI algorithm development.

Required skills

• Python
• SQL
• Large Language Models (LLMs) and embeddings
• Retrieval Augmented Generation (RAG) and vector databases (Pinecone, Weaviate)
• Agentic workflow frameworks (Langgraph, Mastra, LlamaIndex, CrewAI, AutoGen)
• Generative AI platforms (Vertex AI, Bedrock, Azure AI, OpenAI/Anthropic APIs, Hugging Face)
• Cloud data platforms (Databricks, BigQuery, Snowflake, Spark, Postgres, S3, GCS)
• AI evaluation techniques (golden datasets, regression suites, human review loops)