GEN AI Developer

• Gen AI Technologies: Prompt Engineering, LLM Development using Lang chain. Semantic Kernel , Open Source/ API based LLMs ..
• Python Data Science, Machine Learning, Deep Learning, PySpark, PyTorch, TensorFlow, Keras, etc
• AIML Subfields such as Neural Networks, Computer Vision, Speech Processing, Natural Language Processing
• AIML Tools such as AzureML, Google ML, AWS AI/ML, H2O, DataBricks, DataRobots and any other tools

• Gen AI Development :
• Data collection and preparation: Collect and prepare data for training and evaluating LLMs. This may involve cleaning and processing text data, or creating synthetic data.
• Model development: Design and implement LLM models. This may involve choosing the right architecture, training the model, and tuning the hyperparameters.
• Model evaluation: Evaluate the performance of LLM models. This may involve measuring the accuracy of the model on a held-out dataset, or assessing the quality of the generated text.
• Model deployment: Deploy LLM models to production. This may involve packaging the model, creating a REST API, and deploying the model to a cloud computing platform.
• 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.
• Experience in working with ML toolkit like R, NumPy, MatLab etc..
• Experience in data mining, statistical analysis and data visualization
• 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

Role & responsibilities

• LLM Development/Deployment Pipeline creation, workflows
• Study and transform Gen AI PoCs to production grade deployment
• Design machine learning systems, Run machine learning tests and experiments
• Research and implement appropriate ML algorithms and tools
• Develop machine learning applications according to requirements
• Select appropriate datasets and data representation methods
• Perform statistical analysis and fine-tuning using test results
• Train and retrain systems when necessary