Applied AI Engineer - iCloud Data
Tasks
- Architect LLM and agent systems
- Build and operate production AI products
- Build real time AI pipelines with streaming data
- Build retrieval reranking and embedding models
- Create text to SQL and semantic search interfaces
- Design multi step reasoning and tool use orchestration
- Develop prompt engineering and evaluation
- Fine-tune foundation models
- Implement MLOps and LLMOps pipelines
- Implement evaluation harnesses and drift monitoring
- Optimize cost latency throughput and inference quality
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent Frameworks | Agent coordination | Apache Flink | Azure | Batching | Caching | Chroma | Deep learning | Deployment Pipelines | Distillation | Drift monitoring | Embeddings | Evaluation | Faiss | Fine Tuning | Go | Google Cloud | Guardrails | Java | Kafka Streams | LLMOps | Lakehouse Architecture | Langchain | Language Models | Language Processing | Large Language Models | Lifecycle Management | Llamaindex | Loss Functions | MLOps | Machine Learning | Model Lifecycle | Model Lifecycle Management | Model Observability | Multi-Agent | Multi-Agent Coordination | Natural Language | Natural Language Processing | Presto | Prompt engineering | Python | Quantization | Retrieval-Augmented Generation | Scala | Semantic Kernel | Semantic Search | Spark | Spark Streaming | Speculative decoding | Streaming | Telemetry | Text-to-SQL | Tool invocation | Transformers | Trino | Vector Databases | Workflow Orchestration
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Regions
Countries
States
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