Senior AI Engineer

Lantern•Dallas, TX

1d•Hybrid

About The Position

Lantern is seeking a Senior AI Engineer to drive the design, development, and scaling of our Generative AI and agentic systems. In this role, you will take technical ownership of complex LLM-powered applications and multi-agent workflows, set the engineering standard for AI development practices, and partner closely with engineering leadership, product, clinical operations, and data teams to translate strategic priorities into production-grade AI solutions. You are a seasoned AI practitioner who has shipped production LLM and agentic systems, can reason through architectural trade-offs, and brings both depth (GenAI, RAG, agents, LLMOps) and breadth (software engineering, data infrastructure, cloud platforms). Beyond building, you will lead design and code reviews, mentor engineers, and actively shape how Lantern builds and operates AI at scale in a regulated healthcare environment. Locations: We prefer Hybrid - at least 3 days/wk in either our Dallas, TX or New York, NY offices

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 (testing, modular design, code reviews, documentation, version control).
Deep hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Anthropic, Google, etc.) and advanced prompt engineering: chain-of-thought, few-shot, structured outputs, tool-calling, and multi-turn dialogue.
Proven experience designing and deploying production RAG systems: document processing, chunking strategies, embedding models, vector databases, hybrid retrieval, and retrieval evaluation.
Hands-on experience with agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, or custom orchestration) and production deployment of multi-step agent workflows.
Experience with LLM evaluation tooling (e.g., RAGAS, TruLens, DeepEval, or custom frameworks) and systematic approaches to measuring and improving output quality.
Experience with cloud platforms (preferably Azure) and containerized deployment (Docker, Kubernetes); familiarity with LLMOps/MLOps tooling (MLflow, Azure ML, W&B).
Strong communication and collaboration skills; proven ability to lead technical discussions and influence cross-functional partners.
Track record of mentoring engineers and elevating team engineering standards.

Nice To Haves

Experience implementing safety, grounding, and compliance controls for AI systems in regulated industries (healthcare, finance, legal, etc.), including audit logging and PII handling.
Working knowledge of fine-tuning, RLHF, DPO, or parameter-efficient adaptation (LoRA, QLoRA) of open-source models for domain-specific tasks.
Experience with healthcare data, clinical documentation (e.g., clinical notes, ADT feeds), or claims workflows.
Architecture-level experience designing multi-agent systems at scale, including agent coordination patterns, distributed state management, and failure recovery.
Experience with streaming data pipelines, real-time inference, or event-driven AI architectures.
Contributions to open-source AI/LLM projects, applied research publications, or conference presentations.

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: design versioned, testable prompt pipelines; establish team standards for prompt management, evaluation, and continuous improvement.
Lead the selection and integration of embedding models, vector databases (e.g., Azure AI Search, Pinecone, Weaviate), and hybrid retrieval architectures; drive systematic retrieval quality improvement.
Define and implement LLM evaluation frameworks and automated quality benchmarks; establish guardrails, grounding strategies, and hallucination mitigation controls meeting healthcare compliance standards.
Evaluate frontier and open-source models (GPT-4.5, GPT-5.x, Claude, Gemini, Llama, Mistral, etc.); lead model selection decisions and maintain awareness of the evolving AI landscape to inform roadmap choices.
Lead the architecture and implementation of production agentic systems — including multi-agent orchestration, planning, tool-use, memory, and state persistence — using frameworks such as LangGraph, AutoGen, CrewAI, or custom layers.
Design robust human-in-the-loop mechanisms, approval workflows, fallback strategies, and audit trails to ensure agentic systems meet safety, compliance, and clinical trust requirements.
Establish patterns for tool-use and function-calling that allow agents to interact reliably with external APIs, clinical systems, and internal data services.
Define standards for agent observability: trace logging, step-level monitoring, behavioral drift detection, and structured evaluation of multi-step agent runs.
Write production-quality, modular, and well-tested code; set the technical bar through rigorous design and code reviews across the AI engineering team.
Architect and maintain LLM inference services, API integrations, and supporting data pipelines on Azure; drive performance, reliability, and cost optimization.
Define and champion LLMOps practices: prompt versioning, experiment tracking, model registration, A/B testing, CI/CD for AI pipelines, and automated regression testing for LLM outputs.
Establish production monitoring and observability for AI systems: latency, quality scores, cost tracking, safety metrics, and behavioral drift alerting.
Lead technical documentation: architecture decision records, runbooks, model cards, and evaluation playbooks.
Serve as the primary technical partner for product, clinical operations, marketing, and data teams; translate complex requirements into well-scoped, high-impact AI initiatives.
Mentor and grow junior and mid-level engineers through pairing, design reviews, knowledge-sharing, and feedback on AI engineering practices.
Lead architecture discussions and contribute to the AI engineering roadmap; represent the team’s technical perspective in cross-functional planning.
Drive adoption of GenAI and agentic capabilities across the organization by communicating technical concepts clearly to non-engineering stakeholders.