Applied AI Engineer - iCloud Data
Tasks
- Architect production AI products
- Build LLM and agent systems from prototype to production
- Build real time AI with streaming systems
- Build reward models
- Deploy LLM and agent models with MLOps and LLMOps
- Develop text to SQL and semantic search
- Fine-tune foundation models
- Implement guardrails for responsible AI security and reliability
- Implement retrieval reranking and embeddings
- Integrate AI capabilities with data lakehouse and SQL engines
- Monitor drift and model and prompt quality
- Optimize inference for cost latency throughput
- Set up evaluation harnesses and golden sets
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | Apache Flink | Apache Kafka | Azure AI | Azure AI Foundry | Batching | Caching | Chroma | Cost Optimization | Deep learning | Deployment Pipelines | Distillation | Drift monitoring | Embeddings | Evaluation Harness | Faiss | Fine Tuning | Go | Golden sets | Google ADK | Java | Kafka Streams | LLM-as-judge | LLMOps | Lakehouse | Langchain | Language Models | Large Language Models | Latency optimization | Lifecycle Management | Llamaindex | MLOps | Machine Learning | Model Lifecycle | Model Lifecycle Management | Observability | Presto | Prompt engineering | Python | Quantization | Reranking | Retrieval-Augmented Generation | Reward Models | Scala | Semantic Kernel | Semantic Search | Spark | Spark Streaming | Speculative decoding | Text-to-SQL | Throughput Optimization | Transformers | Trino | Vector Databases | Workflow Orchestration
Education
Roles
Regions
Countries
States
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