Chubb is the world’s largest publicly traded property and casualty insurer. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.
The North America Data Analytics team at Chubb is seeking a AI Engineer with 4+ years of industry experience to join our fast-paced, high-energy team. This team is responsible for delivering generative AI solutions to our business partners that will meet business objectives and move-the-needle to improve upon key performance metrics.
As an AI Engineer for North America Data Analytics focusing on Generative AI capabilities, you will develop solutions to complex business problems which combine industry standard practices with innovation. You will focus on building pipelines and designing architecture to facilitate generative AI capabilities. This position offers exposure to a wide variety of analytic tools and technologies. Be ready to meet analytic challenges head-on as you develop and refine generative AI data processes for Chubb.
In this role you will:
Collaborate with business partners and peers within the organization to understand and scope the problem, gather business requirements, and facilitate the data integration process. Work closely with team of data scientists to implement and test machine learning models to solve complex business challenges on Azure platform. Research & implement state of the art modeling approaches including but not limited to zero-shot/few-shot learning, embedding techniques, fine-tuning etc. Ensure high quality code that meets business objectives, quality standards and secure web development guidelines. Integrate with data / document sources using APIs and / or data pipelines. Create excellent working relationships with business partners across the Chubb organization, IT and Analytics peer groups. Effectively communicate with key stakeholders in written, oral and presentation formats. Required: Preferred: Nice to have: Education Bachelor’s / Master’s degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics or Engineering (advanced degree a plus). 3+ years’ experience in Machine Learning (ML), with deep expertise in writing, and reviewing production code in Python. Knowledge of ML frameworks and libraries (such as TensorFlow/Pytorch), and exposure to various ML algorithms and their practical implementation in production at large scale. Experience with designing scalable end-to-end Machine Learning/NLP systems. Experience on distributed, high throughput and low latency architecture. Understanding of NLP techniques around text cleaning/pre-processing, entity extraction, encoder-decoder architectures, similarity matching etc. Experience building software on top of containerization technology (Kubernetes, Docker etc.), and familiarity with frameworks/tools such as FastAPI, Uvicorn. Familiarity with Continuous Integration tools such as Jenkins. Experience with architecting and consuming APIs in a scalable (multi-threaded/batched) fashion. Ability to multi-task, learn new things quickly, and have excellent problem solving & communication skills. A mindset for collaboration, knowledge sharing and keeping up to date on emerging industry trends. Appetite for learning and keeping up to date on the latest generative AI technologies and processes Comfortable with command line (Linux, Windows) scripting. Familiarity with Prompt Engineering Familiarity with generative AI techniques, such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), or other generative models. Experience with at least one other programming language (R, Julia, Scala, Go, Java, C++). Exposure to technologies such as DBT, Apache AirFlow or Luigi. Working knowledge of containers, specifically Dockerizing APIs. Exposure to CI-CD pipelines. Comfortable with at least one NoSQL database (MongoDB, ElasticSearch, CosmosDB, etc.).Location:
We are open to the following work locations for this position: (Philadelphia, PA - Jersey City, NJ - Whitehouse Station, NJ - Simsbury, CT). Please note there will be limited expected travel to various work locations for in person collaboration and meetings.