Machine Learning Engineer
Tasks
- Apply anomaly detection and time series forecasting to operational data
- Automate issue detection and resolution with human in the loop approval
- Build AI powered systems for quality and reliability
- Collaborate with SREs and product teams to design and deploy AI solutions
- Deploy ML models into backend services via APIs
- Design LLM based workflows with prompt engineering tool calling and agentic reasoning
- Implement evaluation frameworks for AI system performance
- Integrate AI capabilities with production infrastructure
- Monitor model performance run continuous evaluation and safety guardrails
- Perform code reviews testing documentation and CI CD pipeline development
- Use vector embeddings similarity retrieval and pattern clustering for incident investigation
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Agentic Workflows | Anomaly Detection | Anthropic Claude | Atlas Vector Search | Azure OpenAI | B testing | ChromaDB | Classification | Clustering | Continuous Evaluation | Embeddings | Evaluation metrics | Faiss | FastAPI | Flask | GitHub | Huggingface | Kubernetes | Langchain | Language Models | Large Language Models | Model Monitoring | MongoDB Atlas | MongoDB Atlas Vector Search | OpenAI API | Pinecone | Precision Recall F1 | Precision-recall | Prometheus | Prompt engineering | PyTorch | Python | Scikit-learn | Series forecasting | Similarity Search | Splunk | TensorFlow | Time Series | Time Series Forecasting | Tool-Calling | Vector Search | Vector embeddings
Education
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