Gen AI Architect

Must Have Technical/Functional Skills

- Be an expert source on machine learning to drive delivery of new and innovative solutions.

- Propose creative solutions to approach business solutions with emerging technologies.

- Prototype new ways of applying technologies for solving business problems.

- Educate others so that they can demonstrate the innovative methods for achieving outcomes.

- Build and maintain machine learning principles, best practices, and code accelerators.

- Conduct external research and internal experimentation for machine learning techniques.

- Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.

Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.

Roles & Responsibilities

- GenAI Application Development Expertise

- Programming

Languages:

Python

- Development Tools:
Lang Chain, Llama Index, Lang Flow, Langgraph, Lang Smith, 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 (Sentence Piece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)

- Datasets:
Hugging Face Datasets, Tensor Flow Datasets

- 3. LLMOps Proficiency

- Infrastructure & CI/CD:
Dev Ops, Kubernetes, Docker, Git, Jenkins, Git Lab, Git Hub Actions

- Monitoring & Management:
Ray, Seldon Core, MLFlow, MLServer, Triton, Bento

ML, Prometheus, Grafana

- 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

- Basic Qualifications

- Bachelor's degree in computer science, Data Science, or a related field; master's degree preferred.

- 5+ years of professional and/or postgraduate academic research experience in software engineering.

- Preferred experience in SAP / Salesforce or Oracle programs.

- 4+ year of experience designing and developing machine learning solutions.

- 3+ years of experience with cloud native engineering, AWS, Azure, Google.

Generic Managerial Skills, If any

- Lead a team of junior developers

- Ability to translate requirements into understandable technical design.

- Task effort estimation and distribution among team members.

- Excellent communication skills, and stakeholder management.

- Ability to work with Cross functional teams and business users.