Overview

We are looking for a Machine Learning Engineer who will be responsible for improving and maintaining our machine learning models/pipelines, developing scalable ETL pipelines, and optimizing the end-to-end data processing workflow. This role requires strong experience in computer vision, OCR, and deep learning, as well as the ability to deploy models in a production environment.

Responsibilities

Design, develop, and maintain data pipelines to manage TIFF images, extracted fields, CSV, PDF, XML, and structured outputs.
Build and optimize ETL workflows for preprocessing (resizing, rotation, denoising) and ML pipeline integration.
Develop, train, evaluate, deploy, and monitor deep learning models in a production environment.
Develop API endpoints and integrate structured data into the existing data keying application.
Implement logging, monitoring, and error-handling mechanisms for model performance and data consistency.
Work with Dockerized deployments to streamline ML and data workflows.
Collaborate with ML engineers, software developers, and quality test engineers to ensure seamless integration.

Qualifications

Bachelor's Degree holder.
3+ years of experience in machine learning, ML engineering, or a hybrid role.
Proficiency in Python for data manipulation, API development, and ML integration.
Ability to write efficient ETL scripts and manage large datasets.
Strong knowledge of database management (SQL, NoSQL, PostgreSQL, or similar).
Familiarity with YOLO-based object detection and OCR processing.
Knowledge of image processing and computer vision tools such as Pillow, OpenCV, pdf2image, etc.
Experience with training and testing machine learning models using Tensorflow, Pyspark, and Scikit-learn.
Hands-on experience with Docker, Kubernetes, and containerized ML workflows.
Experience working with Azure services (Data Factory, Blob Storage, ML Studio, and other compute resources).
Experience with Flask, FastAPI, or other API frameworks.
Knowledge of MLOps best practices for deploying and monitoring ML models in production.
Hands-on experience using Git and Github/Gitlab, and Github Actions.
Exposure to DevOps practices for CI/CD pipelines in ML projects.
Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.

Preferred Skills (Nice To Have)

Experience working in insurance, document processing, or OCR-related applications.
Knowledge of distributed data processing (Spark, Dask, or similar).
Familiarity with NLP and LLM-based models.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

Software Engineer II (Machine Learning)