AI Ops - AI/ML Engineer
Tasks
- Assemble RCA prompt input
- Build Pydantic validation layer
- Build RAG retrieval layer
- Build embedding pipelines
- Define RCA JSON schema
- Design ingestion lifecycle
- Design vector database schema
- Evaluate LLM with judge prompts
- Implement FastAPI routing gate
- Implement chunking strategies
- Maintain golden test set
- Monitor token usage and latency
- Operate pgvector collections
- Run ablation testing
- Run confidence calibration checks
- Run historical backfill pipelines
- Track retrieval quality metrics
- Tune relevance thresholds
- Tune vector indexing strategy
- Version prompts and track changes
Perks/Benefits
Skills/Tech-stack
Ablation Testing | Amazon EKS | Apache Airflow | Approximate Nearest Neighbor | Asyncio | Calibration Error | Celery | Chunking | Claude API | Confidence Calibration | Cosine similarity | Cross-Encoder | Dense Search | Embedding Models | Expected Calibration Error | FastAPI | Golden Test | Golden Test Set | Hybrid search | Judge Prompt | Kafka | Kubernetes | LLM Evaluation | Multi-tenant | Multi-tenant architecture | Nearest neighbor | Observability | PGVector | Pinecone | Platt Scaling | Prompt engineering | Pydantic | Pydantic Validation | Python | RAG | Re-ranking | Retrieval Re-ranking | SQL | SQLAlchemy | SRE | Sparse Search | Structured Output | Temperature Scaling | Tenant Isolation | Tenant architecture | Test set | Vector Database | Weaviate
Education
N/A
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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