AI Specialist II

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 deployment—selecting the right combination of techniques, models, and architectures for each challenge. This is a hands-on role where you'll architect retrieval pipelines, agentic workflows, and evaluation frameworks while collaborating closely with Product Managers, Data Analysts, Software Engineers, and the business teams who use what you build. You'll be deeply technical with modern LLM and GenAI systems, equally comfortable training custom models, designing RAG pipelines, and pushing the boundary of what AI can do for education.

Data Science - Core discipline for extracting insights from structured and unstructured data using scientific methods and algorithms.

Data Pipelines - Essential for designing and managing series of AI processing steps from data ingestion through model inference.

AI Fluency - Understanding capabilities and limitations of LLMs, embeddings, and AI tools to apply them appropriately to problems.

• 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.

• 4+ 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.
• Experience with AI experimentation and evaluation, including comparing approaches, defining quality metrics, analyzing errors, and iterating based on results.
• Working knowledge of identity and access management, encryption and key management, and secure network patterns such as VPC peering, PrivateLink, and mTLS for customer-embedded or regulated environments.
• Experience using Python and SQL to work with data, build analysis workflows, develop AI algorithms, experiment with models, and build AI and ML solutions.