LLM Engineer

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.

Responsibilities:

• Design and implement LLM-based features across Qure's products, from clinician-facing assistants to automated diagnostic flows.
• Build internal tools, APIs, and developer-facing utilities to enable scalable LLM integrations.
• Collaborate with prompt engineers to design modular, composable workflows (e. g., RAG, chaining, structured prompting).
• Evaluate model performance and behavior across tasks using both human feedback and automated metrics.
• Optimize latency, cost, and reliability of LLM systems using caching, batching, and fallback strategies.
• Deploy and monitor LLM services in production, integrating observability and usage analytics.
• Build systems to manage prompt versioning, LLM configurations, and multi-model experimentation.
• Lead cross-functional initiatives and drive end-to-end project execution across engineering, product, and clinical teams.
• Contribute to internal LLM guidelines, safety practices, and developer enablement.
• Foster a culture of collaboration, ownership, and technical excellence in building impactful LLM-driven solutions.

Requirements:

• Bachelor's degree in computer science, information technology, or a related field.
• Solid understanding of LLM architectures, inference workflows, context management, and prompt patterns.
• Strong programming skills in Python and familiarity with frameworks like LangChain, Haystack, or Pydantic AI.
• Experience integrating OpenAI, Anthropic, or open-source models into production systems.
• Familiarity with emerging LLM protocols such as MCP and frameworks that enable agentic behaviours (e. g., autonomous agents, task decomposition, memory systems)
• Proven ability to work across infra, product, and research teams.
• Comfort with cloud platforms (AWS/GCP), containerized deployments (Docker/K8s), and observability tools.
• Solid understanding of how to set up guardrails around LLMs to ensure responsible, safe, and reliable usage in production systems.
• Strong problem-solving, communication, and cross-functional collaboration skills.

Preferred Skills:

• Experience in the healthcare industry, with an understanding of compliance and security requirements.
• Experience building user-facing tools or assistants powered by LLMs.
• Strong sense of ownership and ability to balance experimentation with production-readiness.
• Familiarity with agile methodologies and project management practices.