LLM Engineer

Key Responsibilities:

• Design and implement LLM-based features across Qure's products, including clinician-facing assistants and 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 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 for 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.

Required Skills & Qualifications:

• 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; 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 (e.g., MCP) and frameworks enabling agentic behaviors (autonomous agents, task decomposition, memory systems).
• Proven ability to work across infrastructure, product, and research teams.
• Comfort with cloud platforms (AWS/GCP), containerized deployments (Docker/K8s), and observability tools.
• Strong understanding of LLM guardrails to ensure responsible, safe, and reliable production usage.
• Strong problem-solving, communication, and cross-functional collaboration skills.

Preferred Skills:

• Experience in the healthcare industry, with 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.