Senior AI Engineer

Employer Direct Healthcare

Senior AI Engineer building production GenAI, RAG, and multi-agent systems for Lantern’s specialty healthcare platform. Leading LLM architecture, LLMOps, technical standards, and cross-functional delivery in regulated healthcare.

Posted 8/4/2026full-timeDallas • New York, Texas • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and delivering LLM-powered capabilities, including advanced RAG pipelines and multi-agent orchestration. Proficient in prompt engineering, LLM evaluation frameworks, and production AI/ML systems deployment, with strong collaboration and mentoring skills.

Highest-signal resume keywords

LLM Application DevelopmentPrompt EngineeringProduction AI/ML Systems DeploymentCloud Platform Experience (Azure)Mentoring and Technical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingLLM APIs (OpenAI, Azure OpenAI, Anthropic, Google)RAG System Design and DeploymentAgentic Frameworks (LangChain, LangGraph, AutoGen)LLMOps/MLOps Tooling (MLflow, Azure ML, W&B)

Soft Skills

Strong Communication SkillsCollaboration SkillsTechnical Discussion Leadership

Tools & Technologies

DockerKubernetesVector DatabasesHybrid Retrieval Architectures

Industry Keywords

AI Safety/Compliance ControlsHealthcare DataMulti-Agent ArchitectureStreaming Pipelines

Tech Stack

Tools & technologies

AzureCloudDockerKubernetesPython

About the role

Key responsibilities & impact

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
Select and integrate embedding models, vector databases, and hybrid retrieval architectures
Define LLM evaluation frameworks, quality benchmarks, guardrails, grounding strategies, and hallucination mitigation controls
Evaluate frontier and open-source models and lead model selection decisions
Lead architecture and implementation of production agentic systems with multi-agent 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
Lead architecture discussions and contribute to the AI engineering roadmap
Drive organizational adoption of GenAI and agentic capabilities by communicating with non-engineering stakeholders

Requirements

What you’ll need

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, including 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 such as OpenAI, Azure OpenAI, Anthropic, and Google
Advanced prompt engineering experience, including chain-of-thought, few-shot, structured outputs, tool-calling, and multi-turn dialogue
Proven experience designing and deploying production RAG systems, including document processing, chunking, embeddings, vector databases, hybrid retrieval, and retrieval evaluation
Hands-on experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or custom orchestration
Experience with LLM evaluation tooling such as RAGAS, TruLens, DeepEval, or custom frameworks
Experience with cloud platforms, preferably Azure, and containerized deployment using Docker and Kubernetes
Familiarity with LLMOps/MLOps tooling such as MLflow, Azure ML, and W&B
Strong communication and collaboration skills, with ability to lead technical discussions and influence cross-functional partners
Track record of mentoring engineers and elevating engineering standards
Strong-candidate qualifications include regulated-industry AI safety/compliance controls, fine-tuning or RLHF/DPO/LoRA/QLoRA, healthcare data, scalable multi-agent architecture, streaming pipelines, open-source contributions, research publications, or conference presentations

Benefits

Comp & perks

Medical Insurance
Dental Insurance
Vision Insurance
Short & Long Term Disability
Life Insurance
401k with company match
Paid Time Off
Paid Parental Leave