Lead AI/ML and MLOps Consultant
Tasks
- Build end-to-end ML pipelines
- Coach engineers on ML and MLOps practices
- Create thought leadership content and reference architectures
- Define experiment tracking and model versioning
- Deploy and serve ML models
- Develop technical proposals and LOE
- Ensure governance lineage and access control
- Implement CI/CD for ML pipelines
- Implement Feature Stores
- Lead ML system architecture and implementation
- Lead technical architecture and solutioning for prospects
- Monitor model performance and drift
- Partner in pre sales discovery and scoping
- Set ML engineering standards
- Support partner pipeline technical briefings
- Train and evaluate models
Perks/Benefits
Skills/Tech-stack
AWS | Access Control | Agent Orchestration | Agentic AI | Azure | Azure ML | Batch serving | Bias Evaluation | BigQuery | CI/CD | Calibration | Class imbalance | Confidence Aware UX | CrewAI | Data Lineage | Data Pipelines | Data leakage | Databricks | Drift monitoring | Experiment tracking | Fairness | Feature Engineering | Feature attribution | GCP | Governance | Human-in-the-loop | Hyperparameter Tuning | LIME | LLM Evaluation | Langchain | Langgraph | Llamaindex | MLOps | Machine Learning | Model Cards | Model Deployment | Model Evaluation | Model Explainability | Model Monitoring | Model Registry | Model Serving | Model Training | Model versioning | MongoDB | Multi-Agent | Multi-agent orchestration | Observability | Online Serving | Orchestration | Partial Dependence | RAG | React | Redshift | Reproducibility | SHAP | SageMaker | Snowflake | Streaming Serving | Synapse | The Loop | Tool use | Uncertainty Quantification | Validation | Vector Databases | Vector Search | Vertex AI
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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