Cadence Solutions - Applied AI Engineer
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Requirements
• Bachelor's or Master’s degree in Computer Science, Engineering or related field, or equivalent work experience • 3+ years of software engineering experience, with 2+ years building AI/ML-powered systems in production • Hands-on experience with LLM APIs (OpenAI, Anthropic, open-source models) including prompt engineering, tool use / function calling, and structured outputs • Experience building RAG systems: embeddings, vector stores, retrieval optimization, and grounding • Experience in a high-growth, fast-paced environment with end-to-end ownership from design through production • Experience with agent frameworks or orchestration patterns (tool calling, planners, multi-agent coordination) • Fine-tuning experience (SFT, RLHF, LoRA) on domain-specific tasks • Healthcare or regulated-industry experience (HIPAA, SOC 2, clinical data handling)
Responsibilities
• Design, build, and deploy clinical AI agents that reason over patient context, invoke tools, and generate care recommendations. • Own reliability, observability, and cost efficiency of LLM-powered workflows at scale. • Build and optimize RAG pipelines over clinical knowledge bases, treatment protocols, and real-time patient data. • Develop evaluation frameworks: offline benchmarks, safety tests, regression suites, and LLM-as-judge pipelines wired into CI/CD. • Design multi-step agent orchestration: planning, memory, tool use, error recovery, and human-in-the-loop escalation paths. • Collaborate with clinical, product, and engineering teams to translate patient care needs into AI system design. • Stay at the forefront of AI and engineering best practices, continuously pushing the team to raise the bar on quality, performance, and architecture.
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