Senior Machine Learning Egineer

Job Title: Senior Machine Learning Engineer
Location: Washington DC (Remote)
Employment Type: C2C
Experience Level: 10+ Years (Overall)
Client: Federal Project
Eligibility: Must have at least 2 years of stay in the U.S.
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Technology Stack:
AWS SageMaker Studio, Python, PyTorch, OCR,TensorFlow, TF-IDF, OpenCV
Good to have: AutoGluon and Microsoft DiT (Document Image Transformer)
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Key Responsibilities:
Maintain, optimize, and enhance existing document and image processing systems for code prediction.
Collaborate with cross-functional teams to gather business requirements and translate them into ML solutions.
Monitor system performance and proactively troubleshoot document/image model issues.
Design and conduct machine learning experiments specific to document and image analysis, interpret results, and fine-tune models for performance.
Apply advanced ML techniques to improve document image classification, layout analysis, and document structure understanding.
Integrate transformer-based models with OCR pipelines to improve text extraction from images.
Use AWS SageMaker Studio for training, deploying, and monitoring document/image processing models.
Implement AutoML techniques (e.g., AutoGluon, AWS AutoML) to automate model selection and optimization in document/image use cases.
Apply TF-IDF and other text feature extraction methods to support NLP-driven document classification.
Stay current with the latest advancements in document and image processing within the ML/AI space and integrate applicable improvements.
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Required Qualifications:
Bachelor s or Master s degree in Computer Science, Machine Learning, or a related field.
10+ years of experience in machine learning with a strong focus on document and image processing.
Proven experience building and optimizing ML systems in document processing, OCR, and image understanding.
Proficiency in Python and ML libraries like TensorFlow and PyTorch.
Hands-on experience with Microsoft DiT or similar transformer models tailored to document image understanding.
Deep understanding of self-supervised learning approaches applied to document and image pre-training.
Proficient in OpenCV for image preprocessing tasks such as resizing, feature extraction, and noise reduction.
Practical experience with AWS SageMaker ecosystem and AutoML tools such as SageMaker Autopilot.
Familiarity with AutoGluon for streamlining model optimization and pipeline automation in image/text use cases.
Experience applying NLP techniques like TF-IDF to text extracted from document images.
Strong grasp of cloud platforms (AWS, Azure) and containerization tools like Docker.
Excellent analytical, communication, and cross-functional collaboration skills.