AI Engineer

LTHC

AI Engineer developing machine learning, LLM, and generative AI solutions for Lifetime Healthcare Companies. Building scalable data pipelines and integrating models with enterprise systems to improve member health outcomes.

Posted 9/10/2026full-timeRemote • New York • 🇺🇸 United StatesMid-LevelSenior💰 $65,346 - $117,622 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI and machine learning solutions, including LLM and generative AI applications, while ensuring model performance, explainability, and compliance. Proven ability to collaborate with cross-functional teams and engage with stakeholders to align AI strategies with business objectives.

Highest-signal resume keywords

AI/ML Model DevelopmentLLM and Generative AI ApplicationsCloud-Based ML PlatformsData Pipeline EngineeringModel Performance Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingData ManipulationSQLModel OptimizationAI Model ArchitectureStatistical TechniquesPrompt EngineeringDistributed ComputingAI Model Lifecycle Management

Soft Skills

Problem-SolvingCommunicationMentoringCollaborationLeadership

Tools & Technologies

DatabricksAWS SageMakerAzure MLSparkCI/CDML OpsAPIs

Industry Keywords

AI StrategyModel GovernanceEnterprise ApplicationsBusiness ObjectivesMember Privacy

Tech Stack

Tools & technologies

AWSAzureCloudSparkSQL

About the role

Key responsibilities & impact

Develop artificial intelligence and machine learning solutions to solve business problems and improve member health outcomes
Develop LLM and generative AI applications, machine learning models, NLP solutions, optimization and mathematical programming, and recommendation systems
Build and refine data pipelines for feature engineering and ML model input
Collaborate with data engineering teams to acquire, clean, and prepare training data
Support model evaluation, testing, and performance monitoring in pre-production environments
Work within cloud-based ML platforms such as Databricks to develop and optimize AI models
Collaborate with CI/CD and ML Operations engineers on model deployment and monitoring
Participate in peer code reviews and follow AI software development best practices
Develop and refine prompt engineering techniques for LLM and generative AI applications
Contribute to the AI/ML model lifecycle, ensuring reproducibility, scalability, and maintainability
Translate business objectives into AI/ML formulations and measurable success criteria
Optimize and fine-tune models for performance, explainability, and efficiency
Integrate AI models with enterprise applications, APIs, and data pipelines
Lead discovery and solutioning to identify high-impact AI opportunities
Design and implement scalable AI architectures integrated with enterprise systems
Lead LLM and generative AI initiatives aligned with business needs
Mentor junior team members and foster engineering excellence
Recommend best practices for model governance, versioning, and compliance
Engage leadership and cross-functional teams to align AI strategies with business goals
Maintain member privacy and adhere to corporate policies and Code of Conduct
Maintain regular and reliable attendance; perform other assigned functions

Requirements

What you’ll need

Bachelor's degree required; in lieu of a degree, six (6) years of relevant experience required
Prior professional, co-op, or internship experience developing AI/ML solutions, or relevant coursework
Basic understanding of fundamental ML concepts, algorithms, and statistical techniques
Basic experience working with databases, SQL, and data manipulation
Strong problem-solving skills and willingness to learn
Level II: Hands-on professional experience developing ML models for real-world applications
Level II: Intermediate proficiency with cloud-based ML platforms such as Databricks, AWS SageMaker, or Azure ML
Level II: Intermediate knowledge of model performance monitoring and optimization techniques
Level II: Experience with large-scale data pipelines and distributed computing frameworks such as Spark
Level II: Familiarity with CI/CD and ML Ops/LLM Ops principles
Level II: Experience with large language models and generative AI technologies
Level II: Ability to present technical concepts to technical and non-technical stakeholders
Level III: Significant professional experience and knowledge in AI/ML engineering, including developing models at scale
Level III: Advanced proficiency in AI/ML model architecture, optimization, and explainability
Level III: Advanced experience integrating AI solutions with business applications and APIs
Level III: Extensive experience with LLMs and generative AI in production environments
Level III: Advanced understanding of AI model lifecycle management, governance, and operationalization
Level III: Leadership experience mentoring and guiding AI engineering best practices
Level III: Ability to engage with executives and business leaders to drive AI strategy
Ability to orally communicate
Must be able to travel across the enterprise

Benefits

Comp & perks

Group health and/or dental insurance
Retirement plan
Wellness program
Paid time away from work
Paid holidays
Potential remote work, determined case by case
Home office work for business continuity