AI Ops - AI/ML Engineer
Tasks
- Assemble prompt for structured root cause analysis
- Build RAG retrieval layer
- Build embedding pipelines
- Calibrate confidence scoring framework
- Define RCA JSON schema and validation layer
- Design and operate pgvector schema
- Design ingestion lifecycle for runbooks and incidents
- Implement prompt versioning and RCA monitoring
- Maintain golden test set and run pipeline regression tests
- Route RCA output using FastAPI gate logic
- Run monthly confidence calibration checks
- Track retrieval quality metrics and isolation verification
Perks/Benefits
Skills/Tech-stack
Apache Airflow | Approximate Nearest Neighbor | Asynchronous programming | Calibration Error | Celery | Confidence Calibration | Cosine similarity | Cross-Encoder | Embedding Models | Expected Calibration Error | FastAPI | Golden Test | Golden Test Set | Hybrid search | JSON Schema | Judge Prompt | LLM Evaluation | Nearest neighbor | PGVector | Pinecone | Platt Scaling | Prompt engineering | Pydantic | Python | RAG | Re-ranking | SQL | SQLAlchemy | Structured Output | Temperature Scaling | Test set | Vector Database | Weaviate
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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