The opportunity
Coursera is seeking an AI Specialist to design and build AI systems that transform ambiguous business problems into working prototypes and measurable solutions. You'll work across the full lifecycle of applied AI—from data exploration and experimentation through…
What you'll do
Build and design AI systems by selecting the right combination of data,: retrieval pipelines, agentic workflows with tool use, and structured extraction from unstructured sources.
Architect and implement Retrieval Augmented Generation (RAG) pipelines with: vector databases such as Pinecone or Weaviate for grounded, context-aware AI applications.
Build and maintain agentic AI workflows using frameworks such as Langgraph,: Mastra, LlamaIndex, CrewAI, or AutoGen, including multi-step tool use, planning, and autonomous execution loops.
Design and run evaluation for every AI system you ship, covering accuracy,: hallucination and grounding checks, regression suites, and human review loops.
Work with large-scale structured and unstructured datasets on cloud-native: databases and storage such as S3, Postgres, GCS, BigQuery, and Databricks, with strong SQL and data modeling skills.
Build rapid AI prototypes and experiment with different models, retrieval: strategies, prompts, and approaches to find what works best.
What they're looking for
- + years of experience in Data Science, Applied AI, or Machine Learning, with: experience building data-driven or AI-powered solutions.
- Demonstrated history of taking an AI use case from data exploration and: experimentation through a validated working prototype.
- Hands-on experience with machine learning and modern Generative AI techniques: such as LLMs, embeddings, RAG, semantic search, NLP, recommendation systems, or agentic systems.
- At least 2 production deployments involving LLM-based systems including: fine-tuning, RAG, agentic workflows, or prompt-engineered solutions.
