Experience in Generative AI frameworks and tools, including LangChain, LangGraph, AutoGen, or similar frameworks.
Hands-on experience in Large Language Models, transformer-based systems, LLM-integrated pipelines, and GenAI application development.
Exposure to GANs, diffusion models, and advanced generative AI techniques.
Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
Experience building APIs using Flask, FastAPI, or similar Python-based API frameworks.
Experience in AI/ML use cases across Generative AI, NLP, Computer Vision, Industrial Analytics, automation, or human-computer interaction.
Experience building scalable ML pipelines covering data ingestion, preprocessing, model training, evaluation, deployment, monitoring, and retraining.
Exposure to cloud platforms such as Azure, AWS, or GCP and related AI/ML services.
Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI APIs, or similar AI platforms.
Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, Azure AI Search, or similar.
Experience in developing RAG pipelines, chatbots, document intelligence solutions, knowledge assistants, or enterprise AI applications.
Exposure to Docker, Kubernetes, MLflow, Airflow, Kubeflow, Databricks, or similar tools.
Understanding of model robustness, explainability, fairness, privacy, security, and data governance standards.
Must be well versed or have exposure to Agile methodology and software engineering best practices.
