Coursera: LLMops Engineer — 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 LLMops Engineer in India, under reference 6212857004. 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: 6 October 2026

Responsibilities and work described

• Building: and maintain the operational tooling for our LLM-powered systems, including prompt/pipeline versioning, evaluation harnesses, and CI/CD workflows tailored to non-deterministic AI outputs
• Implementation: monitoring and observability for production LLM systems — tracking latency, token usage, cost per request, output quality, and drift over time
• Design: and run automated evaluation suites to catch regressions, hallucinations, and quality degradation before they reach customers
• Management: RAG pipelines and vector store infrastructure, keeping retrieval sources fresh, accurate, and performant
• Implementation: safety and compliance guardrails — content filtering, PII redaction, and access controls — in line with enterprise data privacy and residency requirements
• Own cost governance for LLM usage: caching strategies, model routing, and usage reporting to keep spend predictable as adoption scales
• Collaboration: closely with Product Managers and Senior Engineers to scope operational requirements and translate them into reliable systems
• Participate in sprint planning, technical grooming, and retrospective discussions
• Education
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering or a related field

Essential requirements and candidate background

• 4+ years of experience in a software engineering role, with a background in Backend Engineering and DevOps fundamentals
• Hands-on experience operating or supporting LLM-powered systems in production (via APIs, RAG pipelines, frontier/open weight/fine-tuned models)
• Proficiency: a backend language such as Python, Java, TypeScript or Go, and familiarity with Docker and container orchestration (Kubernetes)
• Experience implementing APIs, working with SQL and NoSQL databases, and writing automated tests
• Knowledge: at least one cloud platform (AWS, GCP, or Azure), across both managed and self-hosted services
• Familiarity: infrastructure-as-code (e.g., Terraform) and CI/CD tooling (e.g., GitHub Actions, Jenkins)
• Comfort debugging production issues involving non-deterministic systems, with attention to detail and a bias towards reliability
• Belief in engineering quality and building tooling that creates leverage for others

Preferred experience and additional strengths

• Experience: LLMOps-specific tooling such as LangFuse, LangSmith, Weights & Biases-style evaluation frameworks, or vector databases like Pinecone, Weaviate, pgvector
• Knowledge: RAG architectures, MCP implementation & governance, agent orchestration frameworks like LangGraph and LLM Gateways such as LiteLLM, Open Router & enterprise AI ecosystems such as Vertex AI, Bedrock
• Exposure: prompt management and versioning practices treated as code along with model access management, deterministic guardrails and evals
• Understanding: AI governance considerations — data privacy, residency, and compliance in enterprise AI deployments
• Work in a fast-paced, ambiguous environment with a proactive, ownership-driven mindset
• Communication skills and comfort collaborating across engineering, product, and strategy functions
• Why Join Us?
• Work at the operational core of Coursera's AI transformation — solving problems that keep real production AI systems reliable, safe, and cost-effective
• Build hands-on expertise in one of the fastest-growing and most in-demand engineering disciplines
• Join a supportive, innovative team with a culture of continuous learning and improvement
• Be part of a mission-driven company transforming global access to education and upskilling in the AI era
• For more information about how Coursera collects and uses your personal information, please see our Coursera + Udemy Global Applicant Privacy Notice

Location, schedule and contract conditions

• As an AI Platform and LLMOps Engineer, you will join a fast-paced innovation team at Coursera working on AI enablement for our organization and customers
• As a customer-facing focus area, this team builds custom AI-powered solutions tailored for our enterprise customers supporting them in their journey of AI adoption and upskilling beyond just the content on our platforms
• Own the operational backbone that lets our agentic AI systems run reliably in production — from prompt and pipeline versioning to evaluation, monitoring, incident and cost management
• Work closely with a cross-functional team of Software Engineers, AI Specialists and Product Managers – helping turn promising AI prototypes into reliable, observable, and cost-effective production systems
• Advertised location: India

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another LLMops Engineer vacancy is not an offer for reference 6212857004; 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 6212857004.

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
• Java
• TypeScript
• SQL
• NoSQL
• Kubernetes
• CI/CD
• Docker
• AWS
• GCP
• Azure
• MCP
• APIs
• AI/ML
• LLMs
• Google Cloud