Senior AI Engineer
Tasks
- Build document ingestion and chunking pipelines
- Build human in the loop review workflows
- Deploy ML model serving endpoints with monitoring
- Design and maintain vector search and RAG pipelines
- Design production LLM integration pipelines
- Develop and operate AI features for financial data
- Evaluate and integrate AI frameworks and tools
- Implement model performance monitoring and data drift detection
- Implement structured output validation and confidence scoring
- Instrument AI components with structured logging and distributed tracing
- Integrate AI services with backend microservices using API contracts
- Optimize retrieval using embeddings and reranking
- Write testable backend code in Python or Go or Node.js
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | API contracts | AWS | Alerting | Anthropic API | Async Programming | Azure | B testing | Chunking | Circuit Breakers | Cohere | Dashboards | Data Drift | Data Drift Detection | Distributed tracing | Document ingestion | Drift Detection | Embeddings | GCP | GRPC | Go | Graceful Degradation | Huggingface | Human-in-the-loop | Input validation | Kafka | Kinesis | LLM | Langchain | Latency SLO | Llamaindex | MLflow | Model Performance Monitoring | Model Serving | Model performance | Model retraining | Monitoring | Node.js | OpenAI API | Output parsing | PGVector | Performance Monitoring | Pinecone | PostgreSQL | Prompt engineering | Python | RAG | REST API | Reranking | Retrieval-Augmented Generation | SageMaker | Structured Logging | The Loop | Vector Databases | Vector Search | Vertex AI | Weaviate | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
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