{"id":105891,"date":"2026-08-05T06:21:45","date_gmt":"2026-08-05T06:21:45","guid":{"rendered":"https:\/\/jobs.dataaxisnode.com\/kenya\/uncategorized\/llmops-engineer\/"},"modified":"2026-08-05T06:21:45","modified_gmt":"2026-08-05T06:21:45","slug":"llmops-engineer","status":"publish","type":"post","link":"https:\/\/jobs.dataaxisnode.com\/kenya\/jobs\/nairobi\/llmops-engineer\/","title":{"rendered":"LLMOps Engineer"},"content":{"rendered":"<p><script type=\"application\/ld+json\">{\"@context\": \"https:\/\/schema.org\/\", \"@type\": \"JobPosting\", \"title\": \"LLMOps Engineer\", \"description\": \"Role Summary\\nProject A is ALX\u2019s AI learning platform \u2014 a set of LLM products used by learners. Every one generates a stream of LLM data, and every one has hypotheses baked into it about what \u201cworking\u201d means. The LLMOps Engineer owns the analyzer function: turning that stream into an honest answer about whether the products work. Take RAG as one example \u2014 documents must be stored accurately, fetched accurately, and fetched in the right mixture: three separate failure modes, each needing its own eval. Every product decomposes like that. This is a junior-to-mid role with a deliberate growth path: you start close to the technical lead\u2019s designs and grow into full ownership of the function.\\nYou will work in collaboration with Anthropic Engineers, a cross functional\u00a0 team of AI engineers, product managers and data scientists to design world class learning experiences.\\nSpecific Responsibilities\\nEvaluation Suites\\nBuild and run eval suites per product, decomposed by failure mode, running on schedule and on every release \u2014 regression testing so nothing ships if it broke what worked.\\nKeep evals cost-effective as the product line grows.\\nReporting, Data & Collaboration\\nOwn the reporting loop \u2014 findings from evals and platform data in front of the team and stakeholders, including surfacing unintended or problematic model behaviour before learners do.\\nSteward the core datasets the team depends on, including classified customer-support data.\\nPartner with the AI Product Manager on instrumentation \u2014 they instrument the product, you build the evals over what is captured. This is a measurement role, not infrastructure \u2014 no model hosting or serving.\\nSkill Requirements - Essential\\nPython & data: solid Python and a data inclination,\u00a0 comfortable shaping and analysing messy LLM-generated data.\\nDecomposition: the ability to look at an AI product and decompose it into success and failure metrics.\\nEval landscape: familiarity with Langfuse, RAGAS, DSPy, or similar \u2014 depth in one, awareness of the rest. These tools are learnable; we hire the fundamentals underneath them.\\nDesirable not required: experience keeping evals cheap at scale; dashboarding and reporting; classical statistics.\\nEssential Traits for Success\\nYou want to own a function, not execute tickets.\\nYou communicate well and like collaborating,\u00a0 you will support every builder on the team.\\nYou can point to any project, even a small one, where you measured an AI system honestly.\", \"datePosted\": \"2026-08-04\", \"hiringOrganization\": {\"@type\": \"Organization\", \"name\": \"ALX\"}, \"jobLocation\": {\"@type\": \"Place\", \"address\": {\"@type\": \"PostalAddress\", \"addressLocality\": \"Nairobi\", \"addressCountry\": \"KE\"}}, \"directApply\": true, \"validThrough\": \"2026-08-18T23:59:59\", \"employmentType\": \"FULL_TIME\"}<\/script><\/p>\n<p><strong>Company:<\/strong> ALX<\/p>\n<p><strong>Location:<\/strong> Nairobi<\/p>\n<p><strong>Job Type:<\/strong> Full Time , Remote<\/p>\n<p><strong>Apply Before:<\/strong> 2026-08-18<\/p>\n<h3>Job Description<\/h3>\n<p>Role Summary<br \/>Project A is ALX\u2019s AI learning platform \u2014 a set of LLM products used by learners. Every one generates a stream of LLM data, and every one has hypotheses baked into it about what \u201cworking\u201d means. The LLMOps Engineer owns the analyzer function: turning that stream into an honest answer about whether the products work. Take RAG as one example \u2014 documents must be stored accurately, fetched accurately, and fetched in the right mixture: three separate failure modes, each needing its own eval. Every product decomposes like that. This is a junior-to-mid role with a deliberate growth path: you start close to the technical lead\u2019s designs and grow into full ownership of the function.<br \/>You will work in collaboration with Anthropic Engineers, a cross functional\u00a0 team of AI engineers, product managers and data scientists to design world class learning experiences.<br \/>Specific Responsibilities<br \/>Evaluation Suites<br \/>Build and run eval suites per product, decomposed by failure mode, running on schedule and on every release \u2014 regression testing so nothing ships if it broke what worked.<br \/>Keep evals cost-effective as the product line grows.<br \/>Reporting, Data &#038; Collaboration<br \/>Own the reporting loop \u2014 findings from evals and platform data in front of the team and stakeholders, including surfacing unintended or problematic model behaviour before learners do.<br \/>Steward the core datasets the team depends on, including classified customer-support data.<br \/>Partner with the AI Product Manager on instrumentation \u2014 they instrument the product, you build the evals over what is captured. This is a measurement role, not infrastructure \u2014 no model hosting or serving.<br \/>Skill Requirements &#8211; Essential<br \/>Python &#038; data: solid Python and a data inclination,\u00a0 comfortable shaping and analysing messy LLM-generated data.<br \/>Decomposition: the ability to look at an AI product and decompose it into success and failure metrics.<br \/>Eval landscape: familiarity with Langfuse, RAGAS, DSPy, or similar \u2014 depth in one, awareness of the rest. These tools are learnable; we hire the fundamentals underneath them.<br \/>Desirable not required: experience keeping evals cheap at scale; dashboarding and reporting; classical statistics.<br \/>Essential Traits for Success<br \/>You want to own a function, not execute tickets.<br \/>You communicate well and like collaborating,\u00a0 you will support every builder on the team.<br \/>You can point to any project, even a small one, where you measured an AI system honestly.<\/p>\n<h3>How to Apply<\/h3>\n<p>Interested and qualified? Go to ALX on job-boards.greenhouse.io to apply<br \/>Build your CV for free. Download in different templates.<\/p>\n<p><a href=\"https:\/\/www.myjobmag.co.ke\/apply-now\/1296506\" target=\"_blank\" rel=\"noopener\" style=\"display:inline-block;padding:10px 20px;background:#2271b1;color:#fff;text-decoration:none;border-radius:4px;\">Apply Now<\/a><\/p>\n<p><small>Source: MyJobMag<\/small><\/p>\n<p><!-- job-expiry: 2026-08-18 --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Company: ALX Location: Nairobi Job Type: Full Time , Remote Apply Before: 2026-08-18 Job Description Role SummaryProject A is ALX\u2019s AI learning platform \u2014 a set of LLM products used by learners. Every one generates a stream of LLM data, and every one has hypotheses baked into it about what \u201cworking\u201d means. The LLMOps Engineer [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1408,1409],"tags":[1944,1504,1413,1412],"class_list":["post-105891","post","type-post","status-publish","format-standard","hentry","category-jobs","category-nairobi","tag-alx","tag-full-time-remote","tag-job-listing","tag-myjobmag"],"_links":{"self":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/105891","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/comments?post=105891"}],"version-history":[{"count":0,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/posts\/105891\/revisions"}],"wp:attachment":[{"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/media?parent=105891"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/categories?post=105891"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jobs.dataaxisnode.com\/kenya\/wp-json\/wp\/v2\/tags?post=105891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}