Head of Artificial Intelligence (Chicago)

We are partnering with a high-growth, venture-backed technology company operating at the intersection of insurance, artificial intelligence and advanced analytics. Headquartered in Chicago, the business is developing an AI-native technology platform designed to transform how insurers approach underwriting, pricing, risk assessment and claims management.

With approximately 60 employees and ambitious growth plans, the company is seeking an SVP of AI & Data Analytics to take ownership of its AI strategy, technical architecture and data science capabilities.

This is a senior leadership appointment requiring an individual who can operate at both a strategic and deeply technical level. The successful candidate will be responsible for building scalable AI infrastructure, advancing the company's machine learning capabilities and developing production-grade AI solutions that deliver measurable commercial value.

Key Responsibilities

AI & Machine Learning Engineering

• Own the end-to-end AI/ML strategy, architecture and technical roadmap, from experimentation through production deployment.
• Lead the design and development of predictive models for insurance risk scoring, underwriting optimisation, claims severity, fraud detection and pricing.
• Develop and deploy generative AI applications leveraging large language models (LLMs), retrieval-augmented generation (RAG) and agentic AI frameworks.
• Establish robust MLOps practices covering model training, deployment, monitoring, versioning, automated retraining and drift detection.
• Drive the adoption of deep learning, NLP, transformer architectures and advanced statistical modelling where commercially appropriate.

Data Engineering & Platform Architecture

• Architect and scale cloud-native data platforms using AWS, Azure or GCP, with technologies such as Snowflake, Databricks and Apache Spark.
• Oversee the development of robust ETL/ELT pipelines, streaming data architectures and real-time analytics capabilities.
• Design scalable data ingestion frameworks integrating structured and unstructured insurance datasets, third-party APIs and external risk data.
• Establish modern data engineering practices using Python, SQL, dbt, Airflow and distributed processing technologies.
• Drive the development of feature stores, vector databases and reusable data services to support enterprise-grade AI applications.

Generative AI & Intelligent Automation

• Lead the development of LLM-powered applications for document intelligence, policy analysis, claims processing and underwriting assistance.
• Evaluate and implement model architectures using commercial and open-source LLMs, including fine-tuning, model evaluation and inference optimisation.
• Build agentic workflows capable of automating complex, multistep insurance processes with appropriate human oversight.
• Establish frameworks for prompt engineering, RAG evaluation, model observability, guardrails and responsible AI deployment.
• Identify opportunities to commercialise proprietary AI models and differentiated data assets.

Technical Leadership & Strategy

• Build and lead multidisciplinary teams across data science, ML engineering, data engineering and AI platform development.
• Remain technically engaged in architecture reviews, model design, technology selection and complex engineering decisions.
• Partner with Product and Engineering leadership to ensure AI capabilities are embedded into the core product offering.
• Establish standards for model explainability, data privacy, governance, security and regulatory compliance.
• Work directly with executive leadership to prioritise AI investments, evaluate technical opportunities and support the company's long-term growth strategy.

Experience & Requirements

• Significant experience leading AI, Machine Learning, Data Science or Advanced Analytics functions, ideally within a venture-backed technology company.
• Strong hands-on technical background in Python, SQL, machine learning frameworks and modern cloud architectures .
• Deep understanding of supervised and unsupervised learning, deep learning, statistical modelling, NLP and generative AI.
• Experience with ML frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM and scikit-learn.
• Practical knowledge of LLM application development, RAG architectures, vector databases, embeddings and AI orchestration frameworks such as LangChain or LlamaIndex.
• Proven experience designing and operating production ML infrastructure using technologies such as MLflow, Kubeflow, Docker, Kubernetes and CI/CD pipelines.
• Strong knowledge of cloud data ecosystems, including Snowflake, Databricks, Spark, Kafka and modern data orchestration tooling.
• Experience architecting distributed, scalable data platforms and integrating complex datasets from multiple sources.
• Demonstrated success taking AI products from proof of concept to production, with responsibility for performance, reliability, scalability and commercial outcomes.
• Experience building, mentoring and scaling high-performing technical teams within fast-growing organisations.
• Strong understanding of model governance, explainability, bias mitigation and security, particularly within regulated industries.
• Exposure to insurance datasets, actuarial modelling, underwriting, pricing, claims analytics or InsurTech platforms would be highly advantageous.