Machine Learning Engineer

• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security
• Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes
• Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions
• Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns
• Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes
• Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices
• Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts
• Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency
• Monitor and optimize AI/ML model performance, resource utilization, and platform reliability
• Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions
• Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization

AI/ML Engineer

AI Research Scientist

• Drive innovation in model serving and inference architectures for advanced AI systems.
• Focus on optimizing model deployment and inference strategies.
• Work on a wide spectrum of systems, from resource-efficient models to complex, multi-modal architectures.
• Develop, test, and implement novel serving strategies and inference algorithms.
• Engineer robust inference pipelines, establish performance metrics, and resolve bottlenecks in production environments.
• Enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value.

AI Research Engineer – Kernel & Inference Optimization

• Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs
• Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers
• Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks
• Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions
• Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions
• Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution
• Take end-to-end ownership of problems — implementing fixes or coordinating with engineering and infrastructure teams as needed
• Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions

Forward Deployed Machine Learning Engineer

AI Engineer

• Design, develop and deploy AI models, algorithms and systems to enhance productivity
• Architect and implement production-grade multi-agent AI solutions
• Build and maintain end-to-end agentic AI pipelines
• Develop, test, and deploy internal AI applications
• Provide unified observability with logging, metrics, and alerts
• Integrate agentic AI services with existing enterprise systems
• Collaborate with cross-functional teams to align AI projects with business goals

• Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.
• Collaborate with data scientists and engineers to understand model requirements and optimize pipeline processes.
• Automate the training, testing and deployment processes for machine learning models.
• Continuously monitor and maintain model pipelines, ensuring optimal performance, accuracy and reliability.
• Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.
• Ensure compliance with security and data privacy standards in all MLOps activities.

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AI/ML Engineer

AI Research Engineer – Kernel & Inference Optimization

Forward Deployed Machine Learning Engineer

AI Engineer

Senior/Staff Machine Learning Ops Engineer