GenAI Architect

Mandatory Skills: 

1. GenAI Application Development Expertise

• Programming Languages: Python

• Development Tools: Lang Chain, Llama Index, Lang Flow, Flowise

• Techniques: RAG Techniques

• Databases: Vector Databases (Pinecone, Weaviate, Qdrant)

• Additional Technologies: Knowledge Graphs, FastAPI, Streamlit, Gradio

2. Domain Model Fine-Tuning Capabilities

• Languages & Libraries: Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J)

• Tokenization & Frameworks: Tokenizers (SentencePiece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)

• Datasets: HuggingFace Datasets, TensorFlow Datasets

3. LLMOps Proficiency

• Infrastructure & CI/CD: , GitHub Actions

• Monitoring & Management:  Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, GrafanaDevOps, Kubernetes, Docker, Git, Jenkins, GitLab

4. Data Engineering for AI Applications

• Data Processing & Management: Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake

• Workflow Automation: Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations

• Data Catalogs: Amundsen, Collibra, Alation

5. AI-Ready Cybersecurity Knowledge

• Threat Modeling & Security: AI-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools

• Monitoring & Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial
Example Detection Libraries

6. GenAI Guardrails and Ethics

• Ethics & Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries

• Privacy & Security: Privacy-Preserving Machine Learning Libraries, Robustness and Security Tools

• Transparency & Governance: Model Interpretability Libraries, AI Governance Frameworks and Tools