Senior AI Engineer
Tasks
- Add observability with logging and distributed tracing
- Build RAG-architectures
- Build alerting and latency dashboards
- Build vector search pipelines
- Collaborate on AI roadmap and requirements
- Create human in the loop review workflows
- Define API contracts and graceful degradation
- Design LLM integration pipelines
- Design document ingestion and chunking pipelines
- Design model retraining workflows
- Detect data drift and monitor model performance
- Develop structured output validation
- Establish prompt versioning and evaluation
- Implement circuit breakers
- Implement input validation and output monitoring
- Implement prompt engineering and output parsing
- Integrate AI services with backend microservices
- Optimize retrieval quality with embeddings and reranking
- Serve ML models with latency SLOs
- Write backend code for AI integrated features
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | API contracts | AWS | Alerting | Anthropic API | Async | Azure | B testing | Chain orchestration | Chunking | Circuit Breaker | Confidence scoring | Data Drift | Data Drift Detection | Distributed tracing | Document ingestion | Drift Detection | Embeddings | GCP | GRPC | Go | Huggingface | Input validation | Kafka | Kinesis | LLM | Langchain | Latency dashboards | Llamaindex | MLflow | Microservices | Model Monitoring | Model Serving | Node.js | OpenAI API | Output parsing | PGVector | Pinecone | PostgreSQL | Prompt engineering | Python | RAG | REST | Re-ranking | Retrieval-Augmented Generation | SageMaker | Structured Logging | Structured Output | Vector Database | Vertex AI | Weaviate | Weights and Biases
Education
N/A
Roles
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