Job Title

Key Responsibilities:- Develop and maintain backend systems for data processing and analytics - Implement and optimize machine learning models for OCR and NLP applications - Leverage Python and related frameworks to process large datasets efficiently - Work with data engineering teams to integrate data pipelines and data sources into backend systems - Design, implement, and deploy robust, scalable, and secure backend services - Apply Data Science concepts to analyze, interpret, and leverage data for actionable insights - Collaborate with front-end developers to integrate user-facing elements with server-side logic - Stay up-to-date with the latest trends in data science, machine learning, OCR, and NLP technologies - Troubleshoot and resolve issues in data models, pipelines, and production systems - Mentor and guide junior developers and contribute to team knowledge sharingRequired Skills and Qualifications:- 4+years of experience as a Data Scientist or Backend Developer - Proficiency in Python and relevant libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch) - Strong knowledge of Data Science concepts, statistical analysis, and machine learning algorithms - Experience with Optical Character Recognition (OCR) techniques and tools (e.g., Tesseract, OpenCV) - Strong understanding and experience in Natural Language Processing (NLP) concepts (e.g., Named Entity Recognition, Sentiment Analysis, Text Classification) - Experience with backend frameworks and technologies (e.g., Django, Flask, FastAPI) - Familiarity with database systems (SQL, NoSQL) and cloud platforms (AWS, Azure, GCP) - Solid experience with version control (e.g., Git) and agile development processes - Excellent problem-solving and analytical skills - Strong communication skills and the ability to work effectively in a collaborative team environmentPreferred Qualifications:Immediate Joiners are preferred.- Master’s degree or higher in Computer Science, Data Science, or a related field - Experience with deploying machine learning models in production - Familiarity with containerization technologies (e.g., Docker, Kubernetes) - Experience with big data tools and technologies (e.g., Hadoop, Spark)