Machine Learning Engineer Intern Jobs in Storrs, CT

Strategic Analytics is a dynamic and growing team at Arch that drives innovation and transforms how the business operates. We build AI-first, agent-driven products that change how Arch underwrites, services, and learns from its book, combining frontier LLMs, multi-agent systems, retrieval-augmented generation (RAG), evaluation frameworks, and traditional machine learning.Our mission spans agentic automation, decision intelligence, AI-driven insights, and the responsible deployment of AI at scale. With a proven track record of productionizing dozens of high-quality GenAI products over the past three years, we are continuing to scale our impact and are seeking an AVP, AI Engineering to lead the design and application of advanced AI systems within Strategic Analytics.

Reporting to the SVP of AI & Automation, you will lead the development of multi-agent AI solutions that automate complex business decisions across underwriting and claims. This role is focused on applying AI, machine learning, and data science to solve high-value business problems, establish decision frameworks, and ensure AI systems deliver measurable outcomes with high levels of accuracy and trustworthiness.

Key Responsibilities

Lead the design of multi-agent AI systems that coordinate specialized models and agents to solve complex business problems.

Drive the end-to-end delivery of AI-powered underwriting and claims solutions from experimentation through production deployment.

Develop and evaluate decision frameworks that combine LLMs, retrieval systems, machine learning models, business rules, and human review.

Establish methodologies for measuring model performance, calibration, confidence, accuracy, and business impact to determine when automation is appropriate.

Define the conditions under which AI-driven decisions should be trusted, reviewed, or escalated.

Partner with cross-functional teams to integrate AI capabilities into business workflows and operational processes.

Mentor data scientists, AI engineers, and analysts on agentic AI, model evaluation, prompt engineering, and responsible AI practices.

Establish standards for AI evaluation, monitoring, governance, and continuous improvement within the AI & Automation Center of Excellence.

Translate business opportunities into scalable AI solutions that deliver measurable business value.

Required Skills and Experience

7+ years of experience in AI, machine learning, data science, analytics, or related disciplines.

3+ years of people leadership experience.

Demonstrated experience building and deploying production-grade AI, machine learning, or agentic systems beyond proof-of-concept work.

Strong track record of developing supervised learning models (ML and/or GLMs) that have delivered measurable financial impact.

Deep understanding of model evaluation, experimentation, statistical analysis, and decision science.

Strong experience evaluating AI technologies, platforms, and vendors.

Strong Python experience for data science, machine learning, and AI development.

Exceptional problem-solving skills with the ability to frame ambiguous business challenges as analytical and AI opportunities.

Strong communication skills with the ability to influence technical and non-technical stakeholders.

Hands-on experience with modern AI technologies, including LLMs, RAG, agentic systems, evaluation frameworks, and emerging AI tooling.

Desired Skills and Experience

Familiarity with P&C insurance, including underwriting, claims, or the submission lifecycle.

Experience with retrieval-augmented generation (RAG), evaluation frameworks, and structured-output techniques.

Experience applying AI and machine learning within enterprise environments.

Experience leading multidisciplinary teams across data science, AI, analytics, and technology functions.

Demonstrated commitment to staying current with advancements in AI through research, experimentation, publications, conference participation, or contributions to the AI community.

Education

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Analytics, or equivalent practical experience.