AI Machine Learning Scientist

About the role

The AI Machine Learning Scientist is responsible for Artificial Intelligence (AI) scientific and statistical methods to assist with product creation, development and improvement. This role plays a critical role in enabling the responsible and scalable adoption of AI across the enterprise. The role designs, develops, evaluates, and operationalizes AI and machine learning solutions, including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based systems. The scientist helps build reusable AI capabilities, evaluation frameworks, and governance processes that ensure AI systems are reliable, measurable, compliant, and aligned with Responsible AI principles. They work closely with engineering, product, data science, and business teams to translate complex business challenges into production-ready AI solutions.

Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions that address business and operational challenges at enterprise scale.
• Build and maintain infrastructure, pipelines, and services that connect structured and unstructured data sources for AI-driven applications.
• Develop reusable AI capabilities including RAG pipelines, vector search, semantic retrieval, prompt orchestration, and agentic workflows.
• Implement evaluation frameworks and automated testing strategies to measure model quality, accuracy, bias, safety, and performance.
• Establish monitoring, observability, and governance processes to ensure AI systems remain reliable and compliant in production.
• Collaborate with engineering and product teams to integrate AI capabilities into enterprise platforms and applications.
• Drive adoption of Responsible AI practices by implementing evaluation standards, audit-ready documentation, and model governance controls.
• Optimize AI systems for scalability, latency, reliability, and cost efficiency.
• Support experimentation, benchmarking, and model comparison activities to improve decision-making and accelerate AI innovation.
• Partner with cross-functional stakeholders to translate business requirements into production-ready AI capabilities and services.
• Contribute to technical standards, architecture decisions, and best practices for enterprise AI engineering.
• Develop experimental and analytic plans for machine learning algorithms and data modeling processes, use of strong baselines, and ability to accurately determine cause and effect relations.

Requirements

• Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 4 or more years of experience; or any combination of education and experience in configuration management, which would provide an equivalent background.

Preferred Skills, Capabilities and Experiences

• Experience building and deploying LLM- or SLM-based applications in production environments highly preferred.
• Experience developing Retrieval-Augmented Generation (RAG) systems, semantic search, vector databases, embeddings, and prompt engineering techniques highly preferred.
• Experience designing and implementing AI agents, tool-calling workflows, or agentic architectures highly preferred.
• Experience evaluating AI systems using automated evaluation frameworks, benchmarking approaches, and human-in-the-loop review processes highly preferred.
• Experience building scalable AI/ML pipelines and services using cloud-native architectures highly preferred.
• Experience with MLOps practices including CI/CD, model deployment, monitoring, observability, drift detection, and lifecycle management highly preferred.
• Experience with Python and modern AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex, Hugging Face, or equivalent) highly preferred.
• Familiarity with Responsible AI principles, model governance, bias testing, explainability, and auditability requirements highly preferred.
• Experience integrating AI solutions with APIs, enterprise platforms, and distributed systems preferred.
• Experience reviewing, testing, validating, and hardening AI-generated code and AI-assisted development workflows preferred.
• Experience supporting production AI systems, troubleshooting issues, and driving continuous improvement preferred.
• Strong communication and collaboration skills with the ability to influence technical and non-technical stakeholders preferred.
• Healthcare, regulated industry, or enterprise-scale AI experience preferred.