AI Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Create artificial intelligence and machine learning solutions to address business challenges and enhance member health outcomes.

• Design and implement LLM and generative AI applications, machine learning models, NLP solutions, optimization and mathematical programming, and recommendation systems.

• Construct and optimize data pipelines for feature engineering and input for ML models.

• Collaborate with data engineering teams to gather, cleanse, and prepare training data.

• Assist in model evaluation, testing, and performance monitoring within pre-production settings.

• Utilize cloud-based ML platforms such as Databricks to develop and refine AI models.

• Work alongside CI/CD and ML Operations engineers for model deployment and monitoring.

• Engage in peer code reviews and adhere to best practices in AI software development.

• Develop and enhance prompt engineering strategies for LLM and generative AI applications.

• Contribute to the AI/ML model lifecycle, ensuring aspects such as reproducibility, scalability, and maintainability are upheld.

• Convert business objectives into AI/ML formulations and measurable success metrics.

• Optimize and fine-tune models for improved performance, explainability, and efficiency.

• Integrate AI models with enterprise applications, APIs, and data pipelines.

• Drive discovery and solutioning to pinpoint high-impact AI opportunities.

• Design and implement scalable AI architectures that are integrated with enterprise systems.

• Lead LLM and generative AI initiatives that align with business needs.

• Mentor junior team members and promote engineering excellence.

• Suggest best practices for model governance, versioning, and compliance.

• Collaborate with leadership and cross-functional teams to ensure AI strategies are in line with business objectives.

• Uphold member privacy and comply with corporate policies and the Code of Conduct.

• Maintain consistent and reliable attendance, while also performing other assigned duties.

⛳️ Requirements

• A Bachelor's degree is required; alternatively, six (6) years of relevant experience is acceptable in lieu of a degree.

• Previous professional, co-op, or internship experience in developing AI/ML solutions, or equivalent coursework.

• Basic knowledge of essential ML concepts, algorithms, and statistical methods.

• Fundamental experience with databases, SQL, and data manipulation techniques.

• Strong analytical skills and a willingness to learn.

• Level II: Practical experience in developing ML models for real-world applications.

• Level II: Intermediate proficiency with cloud-based ML platforms like Databricks, AWS SageMaker, or Azure ML.

• Level II: Intermediate understanding 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 effectively present technical concepts to both technical and non-technical audiences.

• Level III: Substantial professional experience and knowledge in AI/ML engineering, especially in developing models at scale.

• Level III: Advanced proficiency in AI/ML model architecture, optimization, and explainability.

• Level III: Significant experience in integrating AI solutions with business applications and APIs.

• Level III: Extensive experience with LLMs and generative AI in production settings.

• Level III: In-depth understanding of AI model lifecycle management, governance, and operationalization.

• Level III: Leadership experience in mentoring and guiding AI engineering best practices.

• Level III: Capability to engage with executives and business leaders to influence AI strategy.

• Strong oral communication skills.

• Must be willing to travel across the enterprise.

🏝️ Benefits

• Group health and/or dental insurance.

• Retirement plan.

• Wellness program.

• Paid time off.

• Paid holidays.

• Potential for remote work, assessed on a case-by-case basis.

• Home office arrangements for business continuity purposes.