Artificial Intelligence and Machine Learning Engineer

IT Delivery

Technology->Agentic AI->Agent Engineering,Technology->Generative AI->Prompt Engineering,Technology->Machine Learning->Generative AI • Knowledge of more than one technology • Basics of Architecture and Design fundamentals • Knowledge of Testing tools • Knowledge of agile methodologies • Understanding of Project life cycle activities on development and maintenance projects • Understanding of one or more Estimation methodologies, Knowledge of Quality processes • Basics of business domain to understand the business requirements • Analytical abilities, Strong Technical Skills, Good communication skills • Good understanding of the technology and domain • Ability to demonstrate a sound understanding of software quality assurance principles, SOLID design principles and modelling methods • Awareness of latest technologies and trends • Excellent problem solving, analytical and debugging skills Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras Cloud computing platforms: AWS, Azure, GCP Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings Computer vision: Convolutional neural networks, recurrent neural networks, object detection Robotics: Reinforcement learning, motion planning, control systems Data ethics: Bias in machine learning, fairness in algorithms Responsible AI: Should have proficient knowledge in Responsible AI and Data Privacy principles to ensure ethical data handling, transparency, and accountability in all stages of AI development. Must demonstrate a commitment to upholding privacy standards, mitigating bias, and fostering trust within data-driven initiatives. LLM Pipeline Creation: Strong experience in designing data pipelines, including data preprocessing, feature extraction, and model integration. Familiarity with best practices for creating efficient and scalable pipelines. Leadership Skills: Proven leadership capabilities to guide and mentor a team of developers. Ability to provide technical direction, solve challenges, and inspire innovation within the team.