Qualifications:

• 3+ years of experience in machine learning, ML engineering, or a hybrid role. (combined prof. experience)
• Proficiency in Python for data manipulation, API development, and ML integration. (3 years of experience)
• Ability to write efficient ETL scripts and manage large datasets. (1-2 yrs – any ETL tool)
• Strong knowledge of database management (SQL, NoSQL, PostgreSQL, or similar). (MySQL – 1-2 yrs)
• Familiarity with YOLO-based object detection and OCR processing. (please include in the notes the OCR Related project that they have worked with.
• Knowledge of image processing and computer vision tools such as Pillow, OpenCV, pdf2image, etc. (2+ yrs)
• Experience with training and testing machine learning models using Tensorflow, Pyspark, and Scikit-learn. (all 3 – 3+ yrs)
• Hands-on experience with Docker, Kubernetes, and containerized ML workflows. (1+ yr)
• Experience working with Azure services & Cloud (Data Factory, Blob Storage, ML Studio, and other compute resources). (1+ yrs – AWS/GCP optional)
• Experience with Flask, FastAPI, or other API frameworks. (any/Django –1-2 yrs)
• Knowledge of MLOps best practices for deploying and monitoring ML models in production.
• Hands-on experience using Git and Github/Gitlab, and Github Actions. (1+ yrs – any)

Preferred Skills (Nice to Have):

• Experience working in insurance, document processing, or OCR-related applications.

• Knowledge of distributed data processing (Spark, Dask, or similar).

• Exposure to DevOps practices for CI/CD pipelines in ML projects.

• Familiarity with NLP and LLM-based models.

• AWS/GCP knowledge

Other Job Details:

Work Setup: Hybrid, 1-2x onsite per month

Work Schedule: Midshift (4pm-12mn)

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.

Please refer to job description.