Staff Machine Learning Engineer
Tasks
- Build reusable ML infrastructure for features experimentation deployment monitoring
- Collaborate to deliver production grade ML systems
- Develop RAG and LLM powered diagnostics automation coding agents
- Establish guardrails evaluation and explainability for GenAI systems
- Implement offline and online evaluation frameworks tied to business outcomes
- Implement scalable repeatable approach to AI development
- Lead end to end model development for ETA risk anomaly fraud
- Translate business workflows into ML problem statements
Perks/Benefits
Skills/Tech-stack
APIs | Agentic Workflows | Airflow | Anomaly Detection | Bias detection | Cause analysis | Data Drift | Data Drift Detection | Data Pipelines | Data Quality | Databricks | Deep learning | Distributed Systems | Drift Detection | ETA Prediction | Embeddings | Experimentation | Feature Engineering | Fraud Detection | Kafka | Language Models | Large Language Models | MLOps | Machine Learning | Model Monitoring | Probabilistic Modeling | Python | Retrieval-Augmented Generation | Root Cause Analysis | Root cause | SQL | Snowflake | Spark | Streaming Data | Transformers | Tree-based Models | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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