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Coursera (now merged with Udemy) is seeking an AI Specialist II to design and build production AI systems that transform how people learn and develop skills. This is a hands-on applied AI role focused on turning complex business problems and datasets into working AI solutions, prototypes, and measurable outcomes.
You will work across the full AI lifecycle: data exploration, solution design, rapid prototyping, experimentation, and rigorous evaluation. The role emphasizes modern LLM and GenAI systems, with deep expertise in building AI pipelines, RAG systems, agentic workflows, and custom model training.
Key responsibilities include:
- Designing and building AI systems by selecting appropriate data combinations, retrieval pipelines, agentic workflows with tool use, and structured extraction from unstructured sources
- Architecting and implementing Retrieval Augmented Generation (RAG) pipelines using vector databases (Pinecone, Weaviate) for context-aware applications
- Building and maintaining agentic AI workflows using frameworks such as LangGraph, Mastra, LlamaIndex, CrewAI, or AutoGen, including multi-step tool use, planning, and autonomous execution loops
- Designing and running comprehensive evaluation for every AI system shipped, covering accuracy, hallucination and grounding checks, regression suites, and human review loops
- Working with large-scale structured and unstructured datasets on cloud-native databases and storage (S3, Postgres, GCS, BigQuery, Databricks) with strong SQL and data modeling skills
- Building rapid AI prototypes and experimenting with different models, retrieval strategies, prompts, and approaches
- Partnering with product managers, data engineers, and backend/frontend engineers to translate business problems into well-scoped AI solutions with measurable KPIs
- Documenting architectures, design decisions, runbooks, prompts, evaluation results, and troubleshooting guides
You will work closely with Product Managers, Data Analysts, and Software Engineers, directly supporting business teams who use what you build.
Requirements:
- 4+ years of experience in Data Science, Applied AI, or Machine Learning, with experience building data-driven or AI-powered solutions
- Experience taking an AI use case from data exploration and experimentation through a validated working prototype
- Hands-on experience with 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 (fine-tuning, RAG, agentic workflows, or prompt-engineered solutions)
- Experience with AI experimentation and evaluation, including comparing approaches, defining quality metrics, analyzing errors, and iterating based on results
- Proficiency with Python and SQL to work with data, build analysis workflows, develop AI algorithms, experiment with models, and build AI/ML solutions
Preferred Qualifications:
- Experience with Generative AI platforms and ecosystems such as Vertex AI, Bedrock, Azure AI, OpenAI/Anthropic APIs, Hugging Face, LangGraph, or LangChain
- Experience designing RAG, vector search, tool-calling, MCP, agent orchestration, or multi-step AI workflows and LLM gateways
- Experience with AI evaluation techniques such as golden datasets, LLM-as-judge, regression evaluation, human evaluation, ranking metrics, or automated quality frameworks
- Experience with modern data platforms such as Databricks, BigQuery, Snowflake, Spark, or equivalent technologies
- Familiarity with AI observability, model monitoring, responsible AI, PII handling, prompt injection mitigation, and AI guardrails
