Proficiency in Python and its ML libraries (TensorFlow, PyTorch, Keras, Scikit-learn, etc.).
Experience with other languages/tools like SQL, Pandas, NumPy, and data manipulation libraries.
Generative AI Model Development & Integration:
Design and Develop GenAI Models: Leverage language models like GPT3.5, GPT4, Gemini, Llama, or custom Transformer-based models for generative tasks such as text generation, summarization, question-answering ,RAG and chatbots.
Use frameworks such as Langchain or similar to develop applications that require complex chains of prompts, manage multiple LLM calls, and integrate with external tools or APIs for data retrieval and processing.
Deploy and scale deep learning and GenAI models using cloud platforms (AWS SageMaker, Google Cloud AI, Azure ML).
Set up observability tools (Langsmith, Weights and Biases etc) in GenAI applications. Monitor model behavior, understand errors, and fine-tune the application in production.
Experience in LLMOps practices, including version control (Git), CI/CD pipelines for GenAI models, and experiment tracking with tools like MLflow, Kubeflow, or W&B.
Deep Learning Frameworks:
Strong understanding of deep learning frameworks like TensorFlow, PyTorch, and Keras.
Experience with neural network architectures like CNNs, RNNs, LSTMs, GANs, and Transformers.
Data Engineering:
Experience with data pipelines, data preprocessing techniques, and large-scale datasets.
Knowledge of big data technologies like Hadoop, Spark, and distributed computing.
Cloud & DevOps:
Experience with cloud services for AI/ML (AWS SageMaker, GCP AI, Azure ML).
Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI deployment.
