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# Senior Data Scientist

- **Company:** CYNET SYSTEMS INC.
- **Location:** Washington, DC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Graph Database, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Azure Machine Learning, Pytorch, Transfer Learning, Large Language Models, Snowflake, Generative AI, Containerization, Scikit Learn, Kubernetes, Information Technology, Data Management, Machine Learning Operations, Docker, Databricks
- **Published:** July 12, 2026
- **Apply:** https://www.careerjet.com/jobad/usd1d8efbfffe5a21eed8b0246452a183a

## About the Role

* Master's or PhD in Computer Science, Data Science, Machine Learning, AI, or related field.
 * 8+ years of hands-on experience building and deploying machine learning models in production.
 * Proven expertise with LLMs, transformer architectures, transfer learning, and model fine-tuning.
 * Strong proficiency in Python, PyTorch or TensorFlow, and ML libraries such as Transformers.
 * Experience with cloud ML platforms, containerization (Docker/Kubernetes), and MLOps tools.
 * Solid understanding of statistical modeling, optimization, and evaluation methodologies.
 * Strong communication skills and ability to collaborate in cross-functional, fast-paced environments., * Experience working in fintech, payments, banking, or fraud/risk environments.
 * Background in vector databases, RAG pipelines, and knowledge graph integration.
 * Experience with data privacy, model governance, and Responsible AI frameworks.
 * Contributions to open-source AI/ML communities or research publications.

## Description

* Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications.
 * Fine-tune or adapt open-source and proprietary models.
 * Build high-performance models for NLP, document intelligence, anomaly detection, risk scoring, predictive analytics, and decisioning use cases.
 * Lead experimentation to evaluate model accuracy, scalability, and fairness.
 * Partner with engineering teams to deploy models on cloud-based ML pipelines (Azure, AWS) & data platforms (Databricks & Snowflake).
 * Work with large-scale structured and unstructured datasets across the payments and financial ecosystem.
 * Implement model monitoring, drift detection, and continuous retraining strategies.
 * Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research.
 * Drive POCs and innovation initiatives that enhance AI capabilities and differentiate our products.


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