AI/ML Architect
Tasks
- Architect end to end AI ML and generative AI solutions
- Build ML and LLM training inference evaluation and monitoring pipelines
- Build embedding pipelines
- Conduct client discovery workshops and solution design
- Create reusable AI reference architectures standards and accelerators
- Design multi-agent workflows
- Design production RAG systems and vector search
- Develop AI agents and copilots
- Implement AI governance security compliance observability and model monitoring
- Integrate AI solutions with cloud platforms and enterprise applications
- Lead architecture reviews technical design sessions code reviews and POCs
Perks/Benefits
Skills/Tech-stack
AI Agents | AI Governance | AI Security | APIs | AWS | Azure | Deep learning | ELT | ETL | Embeddings | Event Driven | Event-driven architecture | GCP | Generative AI | Kafka | LLM Evaluation | LLMOps | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Microservices | Model Monitoring | Natural Language | Natural Language Processing | Observability | Prompt engineering | Retrieval-Augmented Generation | Snowflake Cortex | Snowpark ML | Vector Databases | Vector Search
Education
N/A
Roles
AI | AI/ML Architect | Architect | Learning Architect | ML Architect | Machine Learning Architect
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