About the Company: With more than 24 years of experience building innovative digital products and solutions, Tavant provides impactful results to its customers. It has been the frontrunner in driving digital innovation and tech-enabled transformation across a wide range of industries, such as Fintech, Manufacturing, AgTech, Media & Entertainment, and Retail in North America, Europe, and Asia-Pacific. Ours is a challenging workplace where teams are diverse, competitive, and continually searching for tomorrow's technology and brilliant minds to create it. Furthermore, we do not focus just on what we do - we also care about how we do it. So, bring your talent and ambition to make a difference. We will create a world of opportunities for you.

About the Role: We are seeking a Data Scientist / ML Engineer with comprehensive knowledge in AI/ML, strong coding prowess, and an intrinsic passion for R&D. You will be responsible for designing, developing, and deploying machine learning solutions, while also driving innovation through research and experimentation. If you thrive on tackling complex challenges, continuously learning new techniques, and pushing boundaries in AI, this role is for you.

Key Responsibilities: Design and implement ML pipelines—from data collection and preprocessing to training, validation, and deployment. Select the right models (classical ML or deep learning) and optimize them for performance and scalability. Write clean, efficient, and maintainable code in Python (or relevant languages like R, Scala, C++). Utilize ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and adhere to software engineering best practices (version control, CI/CD). Stay updated with the latest AI/ML research, frameworks, and tools; propose and test new ideas or methodologies. Conduct experiments, benchmark against industry standards, and publish or present findings as needed. Analyze large datasets for insights; engineer relevant features and conduct appropriate transformations. Collaborate with data engineering teams to ensure data pipelines are robust and scalable. Work cross-functionally with product managers, software engineers, and domain experts to integrate ML solutions into products. Share knowledge, review code, and assist junior team members in their growth and development. Implement MLOps best practices, including containerization (Docker), orchestration (Kubernetes), and cloud services (AWS, Azure, GCP). Monitor model performance in production; iterate and refine models to adapt to evolving data and requirements. Tackle complex data challenges, optimize algorithms for latency and throughput, and ensure reliable system performance at scale. Identify and address data quality issues, model drift, and other factors affecting solution success.