Lead AI Engineer
Tasks
- Build ML and AI training pipelines
- Build governance for data quality lineage security compliance
- Build inference systems
- Define data platform architecture
- Define delivery plans and milestones
- Define tool use and memory patterns
- Design Agentic AI systems
- Drive experimentation to production
- Ensure reproducibility and system reliability
- Establish CI/CD standards
- Implement MLOps and LLMOps practices
- Implement rag pipelines
- Lead client advisory
- Lead cross-functional engineering teams
- Manage risks and dependencies
- Mentor engineering leaders and shape technical culture
- Orchestrate multi-agent workflows
- Own discovery to production delivery lifecycle
- Set up model monitoring and retraining
- Translate business problems into AI solution blueprints
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent Orchestration | Agentic AI | Artificial Intelligence | Azure | CI/CD | Compliance | Data Governance | Data Lakes | Data Lineage | Data Pipelines | Data Quality | Data Security | Evaluation | Feature Stores | Google Cloud | Inference Systems | LLMOps | Lakehouse | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Model Monitoring | Model retraining | Multi-Agent | Multi-agent orchestration | Observability | Real Time | Real-time Data | Real-time data pipelines | Reproducibility | Retrieval-Augmented Generation | System Reliability | Training pipelines | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Engineering | Head | Head of Engineering
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