Digital Transformation AI Architect

1 week ago

Pushkar, Rajasthan, India beBeeArtificial Full time ₹ 40,00,000 - ₹ 50,00,000
Key Gen AI Architect Role

The organization is seeking a seasoned Gen AI Architect to design and implement cutting-edge AI solutions that drive business growth.

Responsibilities:
  1. AI Expertise:
  • Develop expertise in GenAI application development using Python and various tools such as LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, and Flowise.
  • Utilize RAG techniques to create robust and efficient AI applications.
  • Work with vector databases like Pinecone, Weaviate, and Qdrant to store and retrieve large amounts of data.
  • Stay up-to-date with the latest technologies including Knowledge Graphs, FastAPI, Streamlit, and Gradio.
Model Fine-Tuning Capabilities:
  • Fine-tune domain models using Python and Data Engineering libraries.
  • Use OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J) to enhance model performance.
  • Work with tokenization frameworks like SentencePiece and Hugging Face Tokenizers, as well as fine-tuning frameworks like Hugging Face Transformers and PyTorch Lightning.
  • Utilize datasets from HuggingFace Datasets and TensorFlow Datasets to train and evaluate models.
LLM Operations:
  • Design and implement scalable infrastructure using DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, GitHub Actions, Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, and Grafana.
  • Implement monitoring and management systems to ensure seamless operation of AI applications.
Data Engineering for AI:
  • Design and implement data processing and management systems using Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), and Delta Lake.
  • Automate workflows using Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, and Great Expectations.
  • Implement data catalogs using Amundsen, Collibra, and Alation.
Ai Cybersecurity Knowledge:
  • Design and implement threat modeling and security systems using AI-specific threat modeling tools, secure ML pipeline tools, and API security tools.
  • Implement monitoring and prevention systems using AI security monitoring tools, prompt injection prevention libraries, and adversarial example detection libraries.
GenAI Governance:
  • Implement ethics and fairness frameworks and tools to ensure responsible AI development.
  • Design and implement privacy-preserving machine learning libraries, robustness and security tools, and model interpretability libraries.
  • Establish transparency and governance frameworks and tools to ensure accountability and trustworthiness of AI applications.
Evaluation Metrics:
  • Design and implement evaluation metrics such as Bleu, Rouge, Perplexity, and BERT-Score to measure model performance.

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