AI/ML

Responsibilities:

• Collaborate with cross-functional teams to translate business requirements into actual implementation of models, algorithms, and technologies.
• Execute the product road map and planning of the programs and initiatives as defined by the product owners.
• Independently solve complex business problems with minimal supervision, while escalating more complex issues to appropriate next level.
• Develop and maintain software programs, algorithms, dashboards, information tools, and queries to clean, model, integrate and evaluate data sets.
• Build and optimize pipelines for data intake, validation, and mining as well as modelling and visualization by applying best practices to the engineering of large data sets.
• Develop and implement advanced machine learning algorithms and models for various applications.
• Apply the latest advances in deep learning, machine learning and natural language processing to improve performance of legacy models.
• Customize latest available large language models to develop generative AI solutions for multiple business problems across multiple functional areas.
• Apply A/B testing framework and test model quality.
• Experience in taking the models to Production using Cloud Technologies
• Provide findings and analysis to take informed business decisions.
• Stay updated with the latest developments in AI/ML technologies and contribute to the continuous improvement of our systems.

Requirement:

• Minimum of 3-7 years of relevant work experience.
• Master’s degree in a related field (Statistics, Mathematics or Computer Science) or MBA in Data Science/AI/Analytics
• Experience with database systems such as MySQL, PostgreSQL, or MongoDB.
• Experience in collecting and manipulating structured and unstructured data from multiple data systems (on-premises, cloud-based data sources, APIs, etc)
• Familiarity with version control systems, preferably Git.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Solid understanding of data structures, algorithms, and distributed computing.
• Excellent knowledge of Jupyter Notebooks for experimentation and prototyping.
• Strong programming skills in Python.
• In-depth understanding of machine learning, deep learning & natural language processing (NLP) algorithms.
• Experience with popular machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Knowledge of containerization tools such as Docker.
• Experience in deploying machine learning models in production environments.
• Excellent problem-solving and communication skills.
• Proficient in using data visualization tools such as Tableau or Matplotlib, or dashboarding packages like Flask, Streamlit.
• Good working knowledge of MS PowerPoint and storyboarding skills to translate mathematical results to business insights.