Developer III - AI/ML Engineer

As a Machine Learning Engineer at UST, you will be a part of a collaborative environment, using cutting-edge ML techniques to solve complex business problems across different domains. Your responsibilities will include: - Applying cutting-edge ML techniques to solve complex business problems. - Framing problems and deciding the appropriate approach, whether it's Supervised, Self-Supervised, or Reinforcement Learning (RL). - Data wrangling using techniques like Weak/Distant Supervision, Pseudo-labelling, and robust EDA (Exploratory Data Analysis). - Developing models end-to-end in ML, DL, and RL. - Expertise in Transfer Learning, including N-shot learning and fine-tuning pre-trained models. - Working across various ML/DL verticals such as Time Series Modelling, Vision, NLP, and RL. - Deploying ML/DL models into production, optimizing for performance and scalability. - Collaborating with teams to deliver automated solutions and run experiments using MLOps tools like Kubeflow, Mlflow, Airflow, or SparkML. - Maintaining a GitHub portfolio with original ML repositories and contributing to the Kaggle community. - Publishing original first-author papers in reputed ML journals or conferences, or filing patents related to AI or Automation. As a qualified candidate, you must have the following skills: - Programming expertise in Python or R. - Applied ML Experience with a focus on problem framing and different learning approaches. - Data Wrangling experience with various techniques. - End-to-End Modeling experience in ML, DL, and RL. - Strong Transfer Learning skills. - Proficiency in ML/DL Libraries like TensorFlow or PyTorch. - Hands-on experience with MLOps tools. - Model Deployment experience using various tools. - Active GitHub/Kaggle Portfolio. - Strong problem-solving and mathematical skills. - Research experience in publishing papers in ML conferences or journals. Good-to-have skills include: - Computer Science/IT Background. - Exposure to Statistics & Probability concepts. - Experience with Docker & Kubernetes. - AI or Automation-specific patents. - Additional ML/DL Domains experience. Education Requirement: Bachelor's Degree in Computer Science, Engineering, or a related field.,