{"id":3267020,"url":"https://alion.io/job/qure-ai-llm-engineer","title":"LLM Engineer","company":{"id":2180,"name":"Qure.ai","domain":"qure.ai","url":"https://alion.io/company/qure-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11000,"max_usd":29000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Haystack","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Agile","optional":true}],"status":"live","first_seen_at":"2026-10-26T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T09:35:00Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"We are looking for an LLM engineer to accelerate our generative AI roadmap by building scalable, production-grade systems that leverage large language models across healthcare workflows. This role goes beyond prompt design; you'll own the end-to-end application of LLMs, from architecting internal tools and APIs to ensuring robust performance, usability, and compliance in real-world deployments. If you're passionate about building the foundational tech to make LLMs work reliably in critical settings, we want to talk to you.\n\nResponsibilities: \nDesign and implement LLM-based features across Qure's products, from clinician-facing assistants to automated diagnostic flows.\nBuild internal tools, APIs, and developer-facing utilities to enable scalable LLM integrations.\nCollaborate with prompt engineers to design modular, composable workflows (e. g., RAG, chaining, structured prompting).\nEvaluate model performance and behavior across tasks using both human feedback and automated metrics.\nOptimize latency, cost, and reliability of LLM systems using caching, batching, and fallback strategies.\nDeploy and monitor LLM services in production, integrating observability and usage analytics.\nBuild systems to manage prompt versioning, LLM configurations, and multi-model experimentation.\nLead cross-functional initiatives and drive end-to-end project execution across engineering, product, and clinical teams.\nContribute to internal LLM guidelines, safety practices, and developer enablement.\nFoster a culture of collaboration, ownership, and technical excellence in building impactful LLM-driven solutions.\n\nRequirements: \nBachelor's degree in computer science, information technology, or a related field.\nSolid understanding of LLM architectures, inference workflows, context management, and prompt patterns.\nStrong programming skills in Python and familiarity with frameworks like LangChain, Haystack, or Pydantic AI.\nExperience integrating OpenAI, Anthropic, or open-source models into production systems.\nFamiliarity with emerging LLM protocols such as MCP and frameworks that enable agentic behaviours (e. g., autonomous agents, task decomposition, memory systems)\nProven ability to work across infra, product, and research teams.\nComfort with cloud platforms (AWS/GCP), containerized deployments (Docker/K8s), and observability tools.\nSolid understanding of how to set up guardrails around LLMs to ensure responsible, safe, and reliable usage in production systems.\nStrong problem-solving, communication, and cross-functional collaboration skills.\n\nPreferred Skills: \nExperience in the healthcare industry, with an understanding of compliance and security requirements.\nExperience building user-facing tools or assistants powered by LLMs.\nStrong sense of ownership and ability to balance experimentation with production-readiness.\nFamiliarity with agile methodologies and project management practices.\nSkills\nAWS, GCP, LLM, Python, large language models","description_format":"text","description_chars":2962,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Computer Vision","Medical 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