Artificial Intelligence Engineer

Job description

* Designed, developed, and deployed Machine Learning and Deep Learning models for industrial challenges involving large-scale numerical, categorical, and time series data, as well as images and videos. * Extensive experience in Python programming for statistical analysis, custom algorithm development, and business insight generation using ML/DL models. * Proficient in optimization techniques including Genetic Algorithms, Linear and Quadratic Programming, and others * Skilled in comprehensive data workflows: collection, exploratory analytics, data cleansing, feature selection, and model validation. * Experience in deploying AI/ML solutions using MLOPs on cloud platforms such as Azure and AWS. * Solid knowledge of NLP, Large Language Models, Retrieval-Augmented Generation, and ML algorithms such as Decision Trees, Clustering, Support Vector Machines, Artificial Neural Networks, LSTM, CNN, and YOLO, with awareness of their practical advantages and limitations. * Motivated by continuous learning and mastery of emerging technologies and methodologies in the fields of artificial intelligence and machine learning. * Lead end to end design, development, and deployment of ML/DL solutions for complex business and industrial problems involving structured, time series, and unstructured data. * Define modeling strategies and technical approaches, selecting appropriate ML, DL, Computer Vision, Optimization, and LLM techniques based on problem constraints and business impact. * Oversee data science lifecycle governance, including data acquisition, exploratory analysis, feature engineering, model validation, and performance monitoring. * Drive production grade deployment through MLOps, ensuring scalable, secure, and reliable model operations on cloud platforms such as Azure and AWS. * Lead development of advanced analytics and optimization solutions, translating business objectives into prescriptive and decision support models. * Provide technical leadership and mentoring to data science teams, establishing best practices in Python engineering, experimentation, and model reliability. * Collaborate with stakeholders to translate business needs into AI solutions, clearly communicating assumptions, risks, outcomes, and measurable value. * Continuously evaluate emerging AI/ML and LLM technologies, driving innovation, POCs, and capability building aligned with organizational strategy.