1 month ago
India (city not specified)Mid Level
Responsibilities
- Design and build AI systems using retrieval pipelines, agentic workflows with tool use, structured extraction, and appropriate data sources.
- Architect and implement RAG pipelines with Pinecone and Weaviate for grounded AI applications.
- Build and maintain agentic workflows using frameworks such as LangGraph, Mastra, LlamaIndex, CrewAI, and AutoGen.
- Design and run AI evaluations covering accuracy, hallucination and grounding checks, regression suites, and human review loops.
- Work with structured and unstructured datasets across S3, Postgres, GCS, BigQuery, and Databricks using SQL and data modeling.
- Rapidly prototype AI solutions by experimenting with models, retrieval strategies, prompts, and alternative approaches.
- Partner with product managers, data engineers, and backend/frontend engineers to define AI solutions and measurable KPIs.
- Document architectures, design decisions, runbooks, prompts, evaluation results, and troubleshooting guidance.
Requirements
- At least 4 years of experience in Data Science, Applied AI, or Machine Learning building data-driven or AI-powered solutions.
- Experience taking AI use cases from data exploration and experimentation through validated working prototypes.
- Hands-on experience with machine learning or generative AI techniques including LLMs, embeddings, RAG, semantic search, NLP, recommendation systems, or agentic systems.
- At least two production deployments involving LLM-based systems such as fine-tuning, RAG, agentic workflows, or prompt-engineered solutions.
- Experience with AI experimentation and evaluation, including quality metrics, approach comparison, error analysis, and iteration.
- Experience using Python and SQL for data work, analysis workflows, AI algorithms, model experimentation, and AI/ML solutions.
- Preferred experience with Vertex AI, Bedrock, Azure AI, OpenAI or Anthropic APIs, Hugging Face, LangGraph, LangChain, or equivalent technologies.
- Preferred experience with RAG, vector search, tool-calling, MCP, agent orchestration, multi-step AI workflows, and LLM gateways.
- Preferred experience with golden datasets, LLM-as-judge, regression evaluation, human evaluation, ranking metrics, or automated quality frameworks.
- Preferred experience with Databricks, BigQuery, Snowflake, Spark, or equivalent data platforms.
- Familiarity with AI observability, model monitoring, responsible AI, PII handling, prompt injection mitigation, and AI guardrails.
Tech Stack
Categories
About Coursera
Coursera builds an online learning platform for individuals, enterprises, and academic institutions, offering courses, Professional Certificates, and online degrees from university and industry partners. Its revenue comes from paid courses and subscriptions, enterprise contracts, and tuition-sharing for degree programs. Founded in 2012 by Andrew Ng and Daphne Koller and headquartered in Mountain View, it is publicly traded on the NYSE (COUR). Coursera has combined with Udemy to form a single, larger skills platform.