GenAI Solutions Engineer developing and implementing AI solutions for healthcare at Tempus. Collaborating with sales teams to address customer needs and enhance operational efficiency.
Responsibilities
Embed directly within the sales team to help identify real problems and solve them using agentic GenAI solutions built on the Tempus OS platform
Partner with customer leaders to uncover high-impact use cases where GenAI and Tempus OS can deliver measurable value
Design and implement agent-based solutions — from workflow automation to patient communication, insight generation, or clinical operations
Enable customer leaders by revolutionizing their processes to enhance their healthcare provider engagement and experience through AI-derived actions and insights
Build rapid prototypes / proof-of-concepts to validate ideas and alignment
Work directly with LLMs and agent frameworks (prompt design, chaining, context management, evaluation, and guardrails)
Lead moderate-to-full implementations in collaboration with customer teams or internal engineers
Build integrations between customer data systems, APIs, and Tempus OS services
Configure, test, iterate, tune performance, and ensure reliability
Evangelize best practices in AI safety, observability, and responsible deployment
Serve as the customer’s technical and strategic bridge to Tempus — shaping our roadmap through real-world feedback
Create solution templates, reference architectures, and internal learning artifacts that make future deployments faster and safer
Train business users, power users, and internal champions on interacting with deployed agents
Requirements
5+ years in technical/customer-facing roles (solutions engineering, AI/ML implementation, systems architecture)
Hands-on experience building or deploying GenAI applications (agents, chat interfaces, or LLM-driven workflows)
Strong programming skills (Python, JavaScript or similar) and familiarity with APIs, webhooks, integration patterns, data pipelines
Experience designing or building prototype-to-production AI solutions (not just proof-of-concepts) — including observability, A/B testing, error-handling, scaling
Familiarity with prompt engineering, RAG, model evaluation, and orchestration frameworks (LangChain, LlamaIndex, or similar)
Excellent communication and storytelling skills — able to interface with both clinicians and engineers
Proven ability to drive projects from discovery through delivery, balancing experimentation and execution
Preferred: Experience in healthcare, life sciences, or regulated industries
Understanding of data privacy, PHI handling, and compliance in AI systems
Experience working with customers who have limited engineering resources
Track record of creating demos, proofs of concept, or internal enablement tools that accelerate adoption
Familiarity with frameworks or tools such as LangChain, Retrieval-Augmented Generation (RAG), agent orchestration frameworks
Experience or understanding working with CRM platforms (e.g. Salesforce) and data visualization tools (e.g. Looker, Tableau)
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