{"id":4232302,"url":"https://alion.io/job/coursera-llmops-engineer","title":"LLMops Engineer","company":{"id":1907,"name":"Coursera","domain":"coursera.org","url":"https://alion.io/company/coursera","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLMOps","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"TypeScript","optional":false},{"name":"AWS Bedrock","optional":true},{"name":"LangChain","optional":true},{"name":"Langfuse","optional":true},{"name":"LangGraph","optional":true},{"name":"LangSmith","optional":true},{"name":"LiteLLM","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Vertex AI","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-06T07:30:13Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T10:29:11Z","board_verified":true,"closed_at":null,"days_open":38,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":38},"description":"About Coursera + Udemy\nCoursera and Udemy are now one company, bringing together two mission-driven brands to create the world’s most powerful platform for turning learning into progress. Together, we help more than 300 million learners and 12,000+ enterprise customers build the skills they need for a world being reshaped by AI. Read more about the combined company by visiting our blog.\nWhy join us now?\nAI is transforming how people learn, work, and grow, and the need for new skills has never been greater. Coursera brings trusted content and credentials from leading university and industry partners, while Udemy brings a dynamic skills marketplace and global network of real-world experts. By combining these strengths, we can connect more people and organizations to the skills they need, when they need them.\nShape what comes next\nBy joining our team, you’ll have the opportunity to reshape how the world learns and applies skills-and help millions of people participate in the new economy. Bring your ideas, expertise, and perspective to meaningful work that can make a difference at global scale.\nAbout the Team (Job Location - Pune) :\nAs 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 an internal focus area, this team builds and maintains the infrastructure & tooling required to drive meaningful AI adoption across the organization, providing a platform for non-technical team members to build and deploy tools and transforming entire business functions into AI-native operating models. 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.\nYou will 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. You will 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.\nKey Responsibilities\nBuild 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\nImplement monitoring and observability for production LLM systems - tracking latency, token usage, cost per request, output quality, and drift over time\nDesign and run automated evaluation suites to catch regressions, hallucinations, and quality degradation before they reach customers\nManage RAG pipelines and vector store infrastructure, keeping retrieval sources fresh, accurate, and performant\nImplement safety and compliance guardrails - content filtering, PII redaction, and access controls - in line with enterprise data privacy and residency requirements\nOwn cost governance for LLM usage: caching strategies, model routing, and usage reporting to keep spend predictable as adoption scales\nCollaborate closely with Product Managers and Senior Engineers to scope operational requirements and translate them into reliable systems\nParticipate in sprint planning, technical grooming, and retrospective discussions\nEducation\nBachelor’s or Master’s degree in Computer Science, Computer Engineering or a related field\nBasic Qualifications\n4+ years of experience in a software engineering role, with a solid background in Backend Engineering and DevOps fundamentals\nHands-on experience operating or supporting LLM-powered systems in production (via APIs, RAG pipelines, frontier/open weight/fine-tuned models)\nProficiency in a backend language such as Python, Java, TypeScript or Go, and familiarity with Docker and container orchestration (Kubernetes)\nExperience implementing APIs, working with SQL and NoSQL databases, and writing automated tests\nWorking knowledge of at least one cloud platform (AWS, GCP, or Azure), across both managed and self-hosted services\nFamiliarity with infrastructure-as-code (e.g., Terraform) and CI/CD tooling (e.g., GitHub Actions, Jenkins)\nComfort debugging production issues involving non-deterministic systems, with attention to detail and a bias towards reliability\nStrong belief in engineering quality and building tooling that creates leverage for others\nPreferred Qualifications\nExperience with LLMOps-specific tooling such as LangFuse, LangSmith, Weights & Biases-style evaluation frameworks, or vector databases like Pinecone, Weaviate, pgvector\nWorking knowledge of 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\nExposure to prompt management and versioning practices treated as code along with model access management, deterministic guardrails and evals\nUnderstanding of AI governance considerations - data privacy, residency, and compliance in enterprise AI deployments\nAbility to work in a fast-paced, ambiguous environment with a proactive, ownership-driven mindset\nStrong communication skills and comfort collaborating across engineering, product, and strategy functions\nWhy Join Us?\nWork at the operational core of Coursera's AI transformation - solving problems that keep real production AI systems reliable, safe, and cost-effective\nBuild hands-on expertise in one of the fastest-growing and most in-demand engineering disciplines\nJoin a supportive, innovative team with a strong culture of continuous learning and improvement\nBe part of a mission-driven company transforming global access to education and upskilling in the AI era\nFor more information about how Coursera collects and uses your personal information, please see our Coursera + Udemy Global Applicant Privacy Notice.\nTo 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.\nIf you encounter suspicious recruitment activity, please report it via our Fraudulent Activity Submission Form.\nCoursera is an Equal Opportunity Employer committed to building a welcoming and inclusive workplace. 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