Note: This is a Hybrid role in Cambridge, ONwith 4 days of on-site work and 1 day of WFH work.

THE ROLE

We’re looking for a hands-on AI Engineer who excels at turning messy, raw operational data into actionable predictive models. Ideally someone who thrives on Exploratory Data Analysis (EDA), smart feature engineering, and building simple, effective end-to-end prediction models that deliver real business value. You’ll work closely with operational data that is often unclean, incomplete, or unstructured — and turn it into reliable forecasts, risk predictions, anomaly detection, or optimization models.

Key Responsibilities:

• Perform deep exploratory data analysis on raw operational datasets

• Identify patterns, clean data, and create high-impact features

• Build, validate, and deploy end-to-end predictive models (classification, regression, time-series, etc.)

• Own the full modeling lifecycle: from raw data ingestion to production model

• Collaborate with other software engineers to integrate models into our core Java/Angular platform

• Continuously monitor and improve model performance in production

Must-Have Skills:

Core Engineering

• Java, Spring Boot, REST APIs, Microservices

• Angular, TypeScript, HTML, CSS

• SQL (PostgreSQL / MySQL)

• Docker, Jenkins, Git, CI/CD

AI / Data Engineering

• Python for data analysis & ML

• Machine Learning, Predictive Analytics, Computer Vision

• ETL & streaming pipelines (Kafka, Redis, event-driven systems)

Nice to Have

• Manufacturing, logistics, or industrial systems experience

• Time-series forecasting, anomaly detection, optimization

• Hybrid mobile dev (Cordova / Android)

• Industrial protocols: MQTT, TCP/IP, PLC, OPC