AI Solution Engineer
Tasks
- Apply deidentification and least privilege access
- Assess fairness and discrimination risk
- Build LLM-powered services
- Build auditability with lineage and reproducible code
- Create offline and online evaluation and guardrails
- Deploy agent workflows with tools and APIs
- Design measurement with baselines holdouts AB and quasi experimental methods
- Design multi step AI agents for production
- Develop production code with testing and CI CD
- Document model intended use limitations and monitoring plan
- Engineer features on lakehouse data pipelines
- Engineer prompts context and evaluation sets
- Forecast propensity segmentation and anomaly detection models
- Implement retrieval-augmented generation
- Instrument monitoring tracing and drift alerts
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AI Foundry | Access Management | Agent Orchestration | Anomaly Detection | Authentication | Azure | Azure AI | Azure AI Foundry | Azure OpenAI | B testing | CI/CD | Classification | Databricks | Databricks SQL | Delta Lake | Docker | Drift Detection | Experimental Design | Feature Engineering | Feature Pipeline | Function Calling | Git | Gradient boosting | Guardrails | Human-in-the-loop | Hybrid retrieval | Hypothesis Testing | Identity & Access | Identity & Access Management | Infrastructure as Code | LLMOps | MLOps | MLflow | Machine Learning | Model Evaluation | Monitoring | Multi-Agent | Multi-agent orchestration | Orchestration | Prompt engineering | Propensity Modeling | PySpark | Python | Quasi Experimental | Quasi-Experimental Design | RAG | RBAC | REST API | Regression | SQL | Segmentation | Semantic Search | Series forecasting | Statistical modeling | Supervised Learning | Terraform | The Loop | Time Series | Time Series Forecasting | Tool-Calling | Tracing | Unity Catalog | Vector Search | “as-code”
Education
N/A
Roles
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