AI / Data Scientist

About the Role

We’re looking for a driven AI / Data Scientist with at least 2 years of experience to join our team. You’ll play a key role in developing and deploying machine learning models, working on real-world AI applications, and turning data into actionable insights. This role is ideal for someone who has moved beyond entry-level work and is ready to take ownership of projects while continuing to grow technically.

What You’ll Do • Design, build, and optimize machine learning and AI models • Work with large, complex datasets to extract insights and improve model performance • Develop AI-powered solutions such as recommendation systems, NLP models, or predictive analytics tools • Collaborate with engineering and product teams to deploy models into production environments • Evaluate and monitor model performance, ensuring reliability and scalability • Contribute to data pipelines and feature engineering processes • Stay current with emerging AI trends, including generative AI and LLMs • Build solutions for document image processing using tools like Google Cloud Vision, AWS Tex tract, and OCR libraries • Perform advanced image processing tasks, including manipulating and enhancing images while preserving original metadata, resolution, and archival integrity

What We’re Looking For • Bachelor’s or Master’s Degree in Computer Science, Data Science, AI, or a related field • 2+ years of hands-on experience in data science or AI roles • Strong proficiency in Python • Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) • Solid understanding of statistical methods and machine learning concepts • Experience working with SQL and large datasets • Ability to take ownership of tasks and deliver end-to-end solutions • Strong communication skills and ability to work cross-functionally

Preferable: • Experience with NLP, computer vision, or recommendation systems • Familiarity with LLMs, prompt engineering, or generative AI applications • Experience deploying models using cloud platforms (AWS, GCP, Azure) • Knowledge of MLOps practices (CI/CD, model monitoring, versioning) • Experience with big data tools (Spark, Hadoop)