Coursera: AI Specialist II — India

About Coursera

Coursera operates an online learning platform. It was launched in 2012 by Andrew Ng and Daphne Koller and partners with universities, governments and companies to offer courses, certificate programmes and degrees in fields such as technology, data science and business, to individual learners and institutions. Its employer feed lists roles across product, engineering and commercial functions for this online learning platform.

The position and its setting

Coursera lists AI Specialist II in India, under reference 6207519004. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Partner Success Services
• Employer listing date: 28 September 2026

Responsibilities and work described

• 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
• Work: closely with Product Managers, Data Analysts, and Software Engineers, and directly with the business teams who use what you build
• Building: 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
• Architecture: and implement Retrieval Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate) for grounded, context-aware AI applications
• Building: 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 SQL and data modelling skills
• Building: 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

Essential requirements and candidate background

• 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
• Experience: 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: 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 experience and additional strengths

• Experience: 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: 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: AI observability, model monitoring, responsible AI, PII handling, prompt injection mitigation, and AI guardrails
• For more information about how Coursera collects and uses your personal information, please see our Coursera + Udemy Global Applicant Privacy Notice
• To protect against recruitment fraud, Coursera + Udemy recruiters only communicate via official coursera.org/udemy.com email addresses and never through personal accounts
• We do not accept resumes via email or social media; please submit all applications directly through our careers page
• If you encounter suspicious recruitment activity, please report it via our Fraudulent Activity Submission Form

Location, schedule and contract conditions

• Advertised location: India

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another AI Specialist II vacancy is not an offer for reference 6207519004; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Coursera

Applications follow the official employer page linked below.

Open the official Coursera posting below and match the title and reference 6207519004.

Compare your qualifications and experience with the essential conditions for the India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• SQL
• Spark
• Azure
• MCP
• APIs
• AI/ML
• LLMs
• data analysis
• machine learning
• AI
• recruitment
• training