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About This Role
### About Lantern
Lantern is the specialty care platform connecting people with the best care when they need it most. By curating a Network of Excellence comprised of the nation's top specialists for surgery, cancer care, infusions and more, Lantern delivers excellent care with significant cost savings to employers and their workforces. Lantern also pairs members with a dedicated care team, including Care Advocates and nurses, for the entirety of their care journey, helping them get back to good health, back to their families and back to work. With convenient access to specialists nationwide, Lantern means quality care is within driving distance for most. Lantern is trusted by the nation's largest employers to deliver care to more than 6 million members across the country. Learn more about us at lanterncare.com.
About You:
- You use LOGIC in your decision making and understand that progress is critical to making change. You focus on the execution of your content while balancing a fast\-paced environment and you take the time to celebrate both the small \& big wins.
- INCLUSION is a core tenant of your personal beliefs. A diverse and inclusive environment is incredibly important to you. You understand and desire to be a part of a diverse team with different experiences and perspectives \& you cherish the differences in each individual that you interact with.
- You have the GRIT, drive and ambition to tackle big problems. Big problems require big ideas and a team that supports new ideas.
- You care deeply for your customers are driven to keep HUMANITY in all decisions. Your customers aren't just the individuals using your product. They are the driving factor in your motivation to make a change.
- Integrity guides you in life. Focusing on the TRUTH vs. giving people the answers they want to hear.
- You thrive in a Team Environment. Collaboration is key in innovation and creating change.
These pillars of LIGHT are a reminder to our team that we are making a difference by providing guidance and support in navigating the often complex and confusing landscape of healthcare. We hope that through this LIGHT, individuals can find their way to the best care, resources, and support they need to get back to life.
If this sounds like you, we would love to connect to speak further about career opportunities at Lantern.
Please apply to our role \& someone from our Talent Acquisition Team will reach out to help you navigate our interview process.
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
Responsibilities:
Generative AI \& LLM Architecture
- 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.
Agentic Systems Design \& Leadership
- 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.
Engineering Leadership \& MLOps
- 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.
Cross\-Functional Partnership \& Mentorship
- 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.
Experience \& Qualifications:
- 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.
Strong Candidates Will Also Have
- 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.
Benefits
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Short \& Long Term Disability
- Life Insurance
- 401k with company match
- Paid Time Off
- Paid Parental Leave
Lantern does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lantern, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lantern AI Hiring
Lantern has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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