Senior AI Engineer building production generative AI and agentic systems for Lantern’s specialty healthcare platform.
Leading LLM architecture, LLMOps, technical reviews, and engineering mentorship in regulated healthcare.
Responsibilities
Architect and deliver production LLM-powered capabilities including advanced RAG pipelines, structured extraction, multi-document reasoning, dialogue systems, and domain-specific language models
Own prompt engineering strategy and establish standards for prompt management, evaluation, and continuous improvement
Lead selection and integration of embedding models, vector databases, and hybrid retrieval architectures
Evaluate frontier and open-source models and lead model selection decisions
Lead architecture and implementation of production agentic systems with orchestration, planning, tool use, memory, and state persistence
Design human-in-the-loop mechanisms, approval workflows, fallback strategies, and audit trails
Establish reliable tool-use and function-calling patterns for external APIs, clinical systems, and internal data services
Define agent observability standards including trace logging, monitoring, drift detection, and structured evaluation
Write production-quality, modular, well-tested code and lead design and code reviews
Architect and maintain LLM inference services, API integrations, and Azure data pipelines
Define and champion LLMOps practices including prompt versioning, experiment tracking, model registration, A/B testing, CI/CD, and regression testing
Establish production monitoring and observability for latency, quality, cost, safety, and behavioral drift
Lead technical documentation including architecture decision records, runbooks, model cards, and evaluation playbooks
Partner with product, clinical operations, marketing, and data teams to translate requirements into AI initiatives
Mentor junior and mid-level engineers and elevate engineering standards
Lead architecture discussions, contribute to the AI engineering roadmap, and represent the team’s technical perspective
Drive organizational adoption of GenAI and agentic capabilities
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience
5+ years of experience building and deploying production AI/ML systems, with at least 2–3 years focused on LLM and GenAI applications
Strong proficiency in Python and software engineering fundamentals, including testing, modular design, code reviews, documentation, and version control
Deep hands-on experience with LLM APIs and advanced prompt engineering
Proven experience designing and deploying production RAG systems
Hands-on experience with agentic frameworks and production deployment of multi-step agent workflows
Experience with LLM evaluation tooling and systematic output-quality improvement
Experience with cloud platforms, preferably Azure, and containerized deployment using Docker and Kubernetes
Familiarity with LLMOps/MLOps tooling such as MLflow, Azure ML, and Weights & Biases
Strong communication and collaboration skills, with ability to lead technical discussions and influence cross-functional partners
Track record of mentoring engineers and elevating team engineering standards
Preferred additional experience with regulated-industry AI safety, grounding, compliance controls, fine-tuning, RLHF, DPO, LoRA, QLoRA, healthcare data, multi-agent systems at scale, streaming pipelines, real-time inference, event-driven architectures, open-source contributions, research publications, or conference presentations
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