AI/ML Architect - Talent Pipeline
Tasks
- Architect and deliver end-to-end AI/ML solutions
- Build AI agents and copilots
- Build embedding pipelines
- Build multi-agent workflows
- Conduct production readiness assessments
- Define AI reference architectures and governance frameworks
- Deliver proof of concepts and pre sales support
- Design AI evaluation and monitoring
- Design ML and LLM pipelines for training and inference
- Design RAG systems
- Design vector search solutions
- Estimate project effort and deliver technical presentations
- Implement production AI ML using Snowflake Cortex and Snowpark ML
- Integrate AI solutions with cloud platforms and APIs
- Lead architecture and code reviews
- Lead client discovery workshops and solution design
Perks/Benefits
- N/A
Skills/Tech-stack
AI Evaluation | AI Foundry | AI Governance | AI Security | APIs | AWS | Agentic AI | Amazon Bedrock | Autogen | Azure | Azure AI | Azure AI Foundry | Azure OpenAI | Cloud platform | Compliance | Cortex Agents | Cortex Analyst | Cortex Search | CrewAI | Deep learning | ETL/ELT | Embeddings | Event Driven | Event-driven architecture | Google Cloud | Google Cloud Platform | Kafka | LLMOps | Langchain | Langgraph | Language Models | Language Processing | Large Language Models | Llamaindex | MLOps | Machine Learning | Microservices | Model Monitoring | Native apps | Natural Language | Natural Language Processing | Observability | PGVector | Prompt engineering | RAG | Retrieval-Augmented Generation | Semantic Kernel | Snowflake | Snowflake Cortex | Snowflake Native Apps | Snowpark ML | Streaming systems | Vector Databases | Vector Search | Vertex AI
Education
N/A
Roles
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