LLM Engineer
<\/span><\/span><\/span> Location: Bengaluru<\/span><\/span><\/span><\/b> Experience:<\/span><\/span><\/span><\/b> 2\-5yrs<\/span><\/span><\/span> Type:<\/span><\/span><\/span><\/b> Fulltime\nPermanent<\/span><\/span><\/span> Department:<\/span><\/span><\/span><\/b> Engineering<\/span><\/span><\/span> <\/span><\/span><\/span><\/b> About Us<\/span><\/span><\/span><\/b> Qure.ai<\/span><\/span><\/a><\/span> is a global healthtech\ncompany using artificial intelligence to improve disease detection and patient\noutcomes at scale. Our solutions are deployed across hospitals, diagnostic\nnetworks, and public health programs worldwide.<\/span><\/span> Today, our technology\nhas impacted <\/span>40M+ lives<\/span><\/b> across <\/span>105+ countries<\/span><\/b>, deployed across <\/span>5,200+\nsites<\/span><\/b>, and powered by one of the world\u2019s largest real\-world medical imaging\ndatasets. With <\/span>26 FDA\-cleared indications<\/span><\/b>, we are helping shape the\nfuture of AI\-driven healthcare.<\/span><\/span> We are looking for an\nLLM Engineer to accelerate our generative AI roadmap by building scalable,\nproduction\-grade systems that leverage large language models across healthcare\nworkflows. This role goes beyond prompt design, you\u2019ll own the end\-to\-end application\nof LLMs, from architecting internal tools and APIs to ensuring robust\nperformance, usability, and compliance in real\-world deployments. If you're\npassionate about building the foundational tech to make LLMs work reliably in\ncritical settings, we want to talk to you. <\/span><\/span> <\/span><\/span> What You\u2019ll Own & Drive<\/span><\/span><\/span><\/b> <\/span><\/span><\/span><\/span> <\/span><\/span><\/span><\/span> Core Skills Required<\/span><\/span><\/span><\/b>
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<\/span> \-Design and implement LLM\-based features across Qure\u2019s products,\nfrom clinician\-facing assistants to automated diagnostic flows.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Build internal tools, APIs, and developer\-facing utilities to\nenable scalable LLM integrations.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Collaborate with prompt engineers to design modular, composable\nworkflows (e.g., RAG, chaining, structured prompting).
<\/span><\/span><\/span><\/span><\/span><\/span> \-Evaluate model performance and behavior across tasks using both\nhuman feedback and automated metrics.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Optimize latency, cost, and reliability of LLM systems using\ncaching, batching, and fallback strategies.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Deploy and monitor LLM services in production, integrating\nobservability and usage analytics.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Build systems to manage prompt versioning, LLM configurations, and\nmulti\-model experimentation.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Lead cross\-functional initiatives and drive end\-to\-end project\nexecution across engineering, product, and clinical teams.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Contribute to internal LLM guidelines, safety practices, and\ndeveloper enablement.
<\/span><\/span><\/span><\/span><\/span><\/span> \-Foster a culture of collaboration, ownership, and technical\nexcellence in building impactful LLM\-driven solutions. <\/span><\/span><\/span><\/span>
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