AI Engineer

Title: AI Engineer ML/NLP pipelines using Databricks, Docker/Kubernetes

Location: Jersey City, NJ 07310. Preferably Jersey City, NJ but open to Whitehouse Station, NJ or Madison Ave, NYC (4 days onsite a week)

Job Description

Business Focus:

Client is heavily investing in AI solutions to transform how underwriting and claims operations are handled. The manager s team is focused on integrating LLMs (Large Language Models), document intelligence , and computer vision into their workflows to automate manual processes and improve decision-making efficiency.

Key Insights from Manager Discussion

AI Use Cases & Vision:

• The team wants AI models that analyze and extract information from unstructured data , such as claims documents, policies, and underwriting notes.
• Example use case: Document sifting AI automatically scans and identifies relevant details buried deep in claim files.
• Image-based AI is another major initiative. For example, customers could upload a photo of a roof , and the AI would analyze the condition and make a renewal recommendation based on that data.
• Solutions will directly support underwriting, claims, and data science teams , improving operational decision-making and risk evaluation.
• A 7 8 solution roadmap has been laid out; 4 projects are already in flagship stage and being actively developed.

Technical Requirements from Manager

Core Skills:

• Hands-on experience with Python for production-level ML code.
• Strong knowledge of LLMs (OpenAI or similar) and prompt engineering .
• Experience building and deploying ML/NLP pipelines using Databricks, Docker/Kubernetes, and CI/CD workflows .
• Familiarity with data platforms like Snowflake and modern orchestration tools (e.g., Airflow, Luigi, DBT).
• Experience integrating with APIs and data pipelines for real-time data ingestion and processing.
• Strong understanding of zero-shot/few-shot learning, embedding techniques , and fine-tuning LLMs for business applications.

Nice to Have:

• Exposure to GANs, VAEs , or other generative models.
• Knowledge of NoSQL databases (MongoDB, ElasticSearch, CosmosDB).
• Additional languages: R, Go, Scala, C++, or Java.