AI Specialist II

About the Role

We at Coursera are seeking a highly skilled and motivated AI Specialist, with strong hands on expertise in AI and data, with the ability to work across data exploration, AI solution design, rapid prototyping, experimentation, and evaluation—turning ambiguous business problems and complex datasets into working AI approaches, prototypes, and measurable solutions. This is a hands-on applied AI role for someone who enjoys figuring out how the system should think, not just how the application should be built.

The ideal candidate is deeply technical, hands-on with modern LLM and GenAI systems, with experience building AI pipelines, and driven to push the boundary of what AI can do for education. You should be equally fluent in training a custom model, designing a RAG pipeline, retrieval design, agent workflows, evaluation frameworks. You will work closely with Product Managers, Data Analysts, and Software Engineers, and directly with the business teams who use what you build.

Key Responsibilities

Build and Design AI systems by selecting the right combination of data , including retrieval pipelines, agentic workflows with tool use, and structured extraction from unstructured sources

Architect and implement Retrieval Augmented Generation (RAG) pipelines with vector databases (Pinecone, 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 & storage (S3, Postgres, GCS, BigQuery, Databricks) with strong SQL and data modelling skills.

Build rapid AI prototypes and experiment with different models, retrieval strategies, prompts, and approaches to find what works best.

Partner with product managers, data engineers and backend/frontend engineers to translate business problems into well-scoped AI solutions with measurable KPIs.

Document architectures, design decisions, runbooks, prompts, evaluation results and troubleshooting guides to enable knowledge continuity and team velocity.

Experience:

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 machine learning and/or modern Generative AI techniques such as LLMs, embeddings, RAG, semantic search, NLP, recommendation, 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.

Experience using 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, LangChain or equivalent technologies.

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 working 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.

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