Machine Learning and Artificial Intelligence Scientist
USD 159K-244K Senior-level Full Time
Tasks
- Apply software engineering best practices
- Architect scalable cloud based AI solutions
- Build deploy generative AI and multi agent solutions
- Build lakehouse and data mesh solutions
- Design agentic systems and evaluation plans
- Design develop validate deploy production machine learning models
- Design for cloud portability and resilience
- Diagnose production issues with root cause analysis
- Engineer robust data and feature pipelines
- Establish AI evaluation frameworks
- Evaluate models with technical and business metrics
- Identify high-value business problems
- Implement AI security privacy and governance safeguards
- Implement MLOps and LLMOps practices
- Mentor data scientists and engineers
- Present findings to business and executive stakeholders
- Run controlled experiments and A B tests
- Translate business objectives into analytical problems
Perks/Benefits
- N/A
Skills/Tech-stack
API Management | Access Control | Agent Orchestration | Amazon Bedrock | Amazon SageMaker | Apache Spark | Application Insights | Athena | Azure API | Azure API Management | Azure Container | Azure Container Apps | Azure Data | Azure Data Lake | Azure Data Lake Storage | Azure Data Lake Storage Gen2 | Azure Databricks | Azure Event | Azure Event Hubs | Azure Functions | Azure Key Vault | Azure Kubernetes | Azure Kubernetes Service | Azure Machine Learning | Azure Monitor | Azure OpenAI | BigQuery | CI/CD | Cloud IAM | Cloud Monitoring | Cloud Run | Cloud SQL | Cloud Storage | Cloud logging | CloudWatch | Column-Level Security | Concept drift | Concept drift detection | Container Apps | Data Catalogs | Data Contracts | Data Drift | Data Drift Detection | Data Lake Storage Gen2 | Data Lineage | Data Quality | Dataflow | Defender for Cloud | Delta Lake | Docker | Drift Detection | EKS | EMR | Embeddings | Entra ID | Evaluation Frameworks | Event Hubs | FastAPI | Feature Engineering | Function Calling | Gemini | GitHub Actions | Glue | Google Kubernetes | Google Kubernetes Engine | Guardrails | Helm | Human-in-the-loop | IAM | Integration Testing | Jupyter | Key Vault | Kubernetes | Kubernetes Engine | Kubernetes Service | Lambda | Language Models | Large Language Models | LightGBM | MLflow | Machine Learning | Microservices | Microsoft Defender | Microsoft Defender for Cloud | Model routing | NumPy | OpenAPI | OpenTelemetry | Pandas | Prompt engineering | Pub/Sub | PySpark | PyTorch | Python | REST APIs | Redshift | Retrieval-Augmented Generation | Row Level Security | S3 | SQL | Schema Enforcement | SciPy | Scikit-learn | Secret Manager | Step Functions | Structured outputs | TensorFlow | Terraform | The Loop | Tool use | Unit Testing | Vector Databases | Version control | Vertex AI | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
Artificial Intelligence Engineer | Artificial Intelligence Scientist | Engineer | Intelligence Engineer | Learning Engineer | Machine Learning Engineer | Machine Learning and Artificial Intelligence Scientist | Scientist | Senior Artificial Intelligence Engineer | Senior Machine Learning Engineer
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