Senior AI Engineer
Tasks
- Build document ingestion and chunking pipelines
- Build vector search pipelines and retrieval quality tuning
- Design and build production LLM integration pipelines
- Design model retraining workflows
- Develop RAG and LLM workflows for financial use cases
- Evaluate and integrate AI frameworks and tools
- Implement human in the loop review workflows
- Implement model performance monitoring and data drift detection
- Implement structured output validation and fallback handling
- Instrument AI components with logging and distributed tracing
- Integrate AI services with backend microservices using API contracts
- Partner with product and data science to define AI roadmap and requirements
- Serve ML models with latency SLOs and monitoring
- Write testable backend code for AI integrated features
Perks/Benefits
Skills/Tech-stack
AWS | Alerting | Amazon Kinesis | Anthropic API | Async | Azure | Circuit Breaker | Cohere API | Data Drift | Data Drift Detection | Distributed tracing | Drift Detection | Embeddings | GCP | GRPC | Go | Huggingface | Kafka | LLM | Langchain | Latency monitoring | Llamaindex | MLflow | Machine Learning | Model Monitoring | Node.js | Observability | OpenAI API | PGVector | Pinecone | PostgreSQL | Prompt engineering | Python | RAG | REST API | SageMaker | Structured Logging | Vector Database | Vertex AI | Weaviate | Weights and Biases
Roles
Regions
Countries
States
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