AI Engineer I/II/III

Excellus BCBS•Rochester, NY

2d•Remote

About The Position

Summary The AI Engineer is part of a highly collaborative team that develops cutting-edge machine learning (ML) and artificial intelligence (AI) models to solve complex business challenges and improve member health outcomes. In this role, you will work on high-impact projects involving advanced ML techniques, including large language models (LLMs) and generative AI. You’ll have the opportunity to experiment with state-of-the-art algorithms, push the boundaries of AI capabilities, and contribute to innovative solutions that drive real-world value. Essential Accountabilities Level I Develops Artificial Intelligence and Machine Learning solutions to solve business problems and improve member health outcomes, incorporating (but not limited to): Large language models (LLMs) and generative AI applications, machine learning models, natural language processing (NLP), optimization and mathematical programming and recommendation systems. Builds and refines data pipelines for feature engineering and ML model input, ensuring efficient and scalable data handling. Collaborates with data engineering teams to acquire, clean, and prepare data for model training. Supports model evaluation, testing, and performance monitoring in pre-production environments. Works within cloud-based ML platforms (e.g., Databricks) to develop and optimize AI models. Understands ML Operations principles and collaborates with CI/CD and ML Operations engineers for model deployment and monitoring. Participates in peer code reviews and follows best practices for software development in AI. Stays up to date with industry trends and new developments in AI/ML. Develops and refines prompt engineering techniques for optimizing interactions with LLMs and generative AI applications. Consistently demonstrates high standards of integrity by supporting the Lifetime Healthcare Companies’ mission and values, adhering to the Corporate Code of Conduct, and leading to the Lifetime Way values and beliefs. Maintains high regard for member privacy in accordance with the corporate privacy policies and procedures. Regular and reliable attendance is expected and required. Performs other functions as assigned by management. Level II (in addition to Level I accountabilities): Contributes to the AI/ML model lifecycle, ensuring reproducibility, scalability, and maintainability of solutions. Works with stakeholders to translate business objectives into AI/ML formulations and measurable success criteria. Optimizes and fine-tunes ML models for performance, explainability, and efficiency. Develops solutions using large language models (LLMs) and generative AI frameworks. Supports the integration of AI models with enterprise applications, APIs, or data pipelines. Engages in continuous learning and shares knowledge on new ML techniques and best practices. Enhances team efficiency through the adoption of automation tools for model training, evaluation, and monitoring. Level III (in addition to Level II accountabilities): Leads the discovery and solutioning process, working with company stakeholders to identify high-impact AI opportunities. Designs and implements scalable AI architectures that integrate with enterprise systems and support business operations. Leads initiatives related to large language models (LLMs) and generative AI, ensuring alignment with business needs. Mentors junior team members and fosters a culture of engineering excellence. Collaborates with Operations and CI/CD teams to improve AI model deployment pipelines and monitoring strategies. Recommends and influences best practices for AI model governance, versioning, and compliance. Engages with leadership and cross-functional teams to align AI strategies with business goals.

Requirements

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 a willingness to learn.
Hands-on professional experience developing ML models for real-world applications.
Intermediate proficiency with cloud-based ML platforms (e.g., Databricks, AWS SageMaker, or Azure ML).
Intermediate knowledge of model performance monitoring and optimization techniques.
Experience working with large-scale data pipelines and distributed computing frameworks (e.g., Spark).
Familiarity with CI/CD and ML Ops/ LLM Ops principles to collaborate effectively with deployment teams.
Experience working with large language models (LLMs) and generative AI technologies.
Ability to present clear and concise technical concepts to both technical and non-technical stakeholders.
Significant professional experience and knowledge in AI/ML engineering with a track record of developing models at scale.
Advanced proficiency in AI/ML model architecture, optimization, and explainability techniques.
Advanced experience integrating AI solutions with business applications and APIs.
Extensive experience working with large language models (LLMs) and generative AI in production environments.
Advanced understanding of AI model lifecycle management, governance, and operationalization.
Leadership experience in mentoring and guiding AI engineering best practices.
Strong ability to engage with executives and business leaders to drive AI strategy.

Responsibilities

Develops Artificial Intelligence and Machine Learning solutions to solve business problems and improve member health outcomes, incorporating (but not limited to): Large language models (LLMs) and generative AI applications, machine learning models, natural language processing (NLP), optimization and mathematical programming and recommendation systems.
Builds and refines data pipelines for feature engineering and ML model input, ensuring efficient and scalable data handling.
Collaborates with data engineering teams to acquire, clean, and prepare data for model training.
Supports model evaluation, testing, and performance monitoring in pre-production environments.
Works within cloud-based ML platforms (e.g., Databricks) to develop and optimize AI models.
Understands ML Operations principles and collaborates with CI/CD and ML Operations engineers for model deployment and monitoring.
Participates in peer code reviews and follows best practices for software development in AI.
Stays up to date with industry trends and new developments in AI/ML.
Develops and refines prompt engineering techniques for optimizing interactions with LLMs and generative AI applications.
Consistently demonstrates high standards of integrity by supporting the Lifetime Healthcare Companies’ mission and values, adhering to the Corporate Code of Conduct, and leading to the Lifetime Way values and beliefs.
Maintains high regard for member privacy in accordance with the corporate privacy policies and procedures.
Regular and reliable attendance is expected and required.
Performs other functions as assigned by management.
Contributes to the AI/ML model lifecycle, ensuring reproducibility, scalability, and maintainability of solutions.
Works with stakeholders to translate business objectives into AI/ML formulations and measurable success criteria.
Optimizes and fine-tunes ML models for performance, explainability, and efficiency.
Develops solutions using large language models (LLMs) and generative AI frameworks.
Supports the integration of AI models with enterprise applications, APIs, or data pipelines.
Engages in continuous learning and shares knowledge on new ML techniques and best practices.
Enhances team efficiency through the adoption of automation tools for model training, evaluation, and monitoring.
Leads the discovery and solutioning process, working with company stakeholders to identify high-impact AI opportunities.
Designs and implements scalable AI architectures that integrate with enterprise systems and support business operations.
Leads initiatives related to large language models (LLMs) and generative AI, ensuring alignment with business needs.
Mentors junior team members and fosters a culture of engineering excellence.
Collaborates with Operations and CI/CD teams to improve AI model deployment pipelines and monitoring strategies.
Recommends and influences best practices for AI model governance, versioning, and compliance.
Engages with leadership and cross-functional teams to align AI strategies with business goals.