Sr Machine Learning Engineer, AI Studio
Tasks
- Build production machine learning pipelines
- Conduct risk assessment and Responsible AI checks
- Define AI product requirements
- Deploy and stabilize AI services
- Design AI system architecture
- Develop evaluation and testing pipelines
- Ensure security and privacy controls
- Implement GenAI RAG and agent features
- Manage incidents and runbooks
- Monitor drift and system performance
- Set up MLOps and LLMOps workflows
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS | Agentic AI | Airflow | Anomaly Detection | Bedrock | Bias Mitigation | CI/CD | Calibration | Causal Inference | Containers | Data Analysis | Data Contracts | Data Lineage | Data Persistence | Databricks | Disaster Recovery | Distributed Systems | Drift monitoring | Embeddings | Ensembles | Event Driven | Event-driven architecture | Explainability | Exploratory Data Analysis | Feature Engineering | FinOps | Fine Tuning | Graph Databases | GxP | Infrastructure as Code | Kubeflow | Kubernetes | LLMOps | MLOps | MLflow | Model Risk Management | Model risk | NLP | Observability | Performance optimization | Predictive Modeling | Privacy | Prompting | Provenance | Python | Red Teaming | Repository Governance | Retrieval-Augmented Generation | Risk Management | Robustness Testing | SLOs | SQL | SageMaker | Serverless | Spark | Structured Output | Supervised Learning | Testing | Threat modeling | Transformers | Uncertainty Estimation | Unsupervised Learning | Vector Databases | Workflow Orchestration | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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