Lead AI/ML and MLOpos Consultant
Tasks
- Build feature stores
- Coach engineers on ML and MLOps practices
- Deploy and serve ML models
- Develop technical briefings for opportunities
- Establish ML CI CD pipelines
- Implement data ingestion pipelines
- Implement experiment tracking
- Lead end to end ML system architecture
- Lead technical architecture pre sales conversations
- Manage model registry and versioning
- Monitor model performance and drift
- Provide technical thought leadership content
- Scope ML projects and prepare estimates
- Set ML engineering standards for governance and reproducibility
- Support partner aligned solutioning
- Train and evaluate ML models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Access Control | Agent Orchestration | Agentic AI | Amazon SageMaker | Azure | Azure Machine Learning | Bias Evaluation | BigQuery | CI/CD | CI/CD for ML | Calibration | Confidence Aware UX | CrewAI | Data Pipelines | Data Quality | Data Warehouse | Databricks | Drift monitoring | Experiment tracking | Fairness | Feature Engineering | GCP | Governance | Human-in-the-loop | Hyperparameter Tuning | LIME | LLM Evaluation | Lakehouse | Langchain | Langgraph | Lineage | Llamaindex | MLflow | Machine Learning | Model Drift | Model Evaluation | Model Explainability | Model Observability | Model Registry | Model Serving | Model Training | Model drift monitoring | Model versioning | MongoDB | Multi-Agent | Multi-agent orchestration | NoSQL | Plan-and-Execute | Python | RAG | React | Redshift | Relational databases | Reproducibility | Responsible AI | SHAP | Snowflake | Synapse Analytics | The Loop | Tool use | Uncertainty Quantification | Vector Databases | Vector Search | Vertex AI | Weights and Biases
Education
N/A
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