Senior AI Architect

1 week ago

Aligarh, Uttar Pradesh, India beBeeArtificialIntelligence Full time ₹ 1,80,00,000 - ₹ 2,50,00,000

We invite you to explore a challenging opportunity as a Gen AI Architect.

Key Responsibilities:
  1. GenAI Application Development Expertise:
  • Develop applications using Python and programming languages with expertise in GenAI
  • Utilize development tools like LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, and Flowise
  • Employ RAG Techniques for efficient application development
  • Work with vector databases such as Pinecone, Weaviate, and Qdrant
  • Explore additional technologies including Knowledge Graphs, FastAPI, Streamlit, and Gradio
Domain Model Fine-Tuning Capabilities:
  • Apply fine-tuning capabilities to domain models using Python and data engineering libraries
  • Work with OSS LLMs like Llama2, Mixtral, GPT-Neo, and GPT-J
  • Use tokenization and fine-tuning frameworks like SentencePiece, Hugging Face Tokenizers, Hugging Face Transformers, and PyTorch Lightning
  • Engage with datasets such as HuggingFace Datasets and TensorFlow Datasets
LLMOps Proficiency:
  • Master the skills of infrastructure and CI/CD with DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, and GitHub Actions
  • Monitor and manage systems using Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, and Grafana
Data Engineering for AI Applications:
  • Process and manage data using Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), and Delta Lake
  • Automate workflows with Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, and Great Expectations
  • Manage data catalogs with Amundsen, Collibra, and Alation
AI-Ready Cybersecurity Knowledge:
  • Develop threat modeling and security skills using AI-specific threat modeling tools, secure ML pipeline tools, and API security tools
  • Implement monitoring and prevention measures using AI security monitoring tools, prompt injection prevention libraries, and adversarial example detection libraries
GenAI Guardrails and Ethics:
  • Apply ethics and fairness frameworks and tools to ensure bias detection and mitigation, fairness metrics, privacy preservation, and robustness
  • Utilize model interpretability libraries and AI governance frameworks and tools for transparency and governance
Monitoring & Evaluation:
  • Measure performance with Bleu, Rouge, Perplexity, BERT-Score, and other evaluation metrics

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