Lead - AI/ML Engineer (Generative AI Agent Systems)
Tasks
- Advise AI governance and model lifecycle management
- Define human in the loop governance
- Define idempotent processing strategies
- Define model evaluation methodologies
- Define reusable delivery frameworks
- Design event-driven distributed systems
- Design generative AI solutions
- Develop prompt engineering workflows
- Establish performance metrics and safety thresholds
- Implement AI agents
- Implement context management patterns
- Lead AI agent architecture
- Lead proof of concept delivery
- Lead shadow mode testing
- Lead technical discovery sessions
- Mentor technical teams
- Monitor model drift and compliance
- Set up continuous evaluation frameworks
- Translate business requirements into AI architectures
Perks/Benefits
- Career growth opportunities
- Flexible work environment
- Inclusive and diverse work culture
- Opportunities for experimentation
Skills/Tech-stack
AI Deployment | AI Governance | Agent Development | Agent Development Kit | Agent Engine | Agent systems | Autonomous Agents | Cloud Security | Compliance Auditing | Concurrent programming | Context Management | Continuous Evaluation | Distributed Systems | Drift monitoring | Event Driven | Event-driven architecture | Function Calling | Generative AI | Go | Golang | Idempotent Processing | Identity Management | Language Models | Large Language Models | Lifecycle Management | Model Evaluation | Model Lifecycle | Model Lifecycle Management | Model Tuning | Monitoring | Multi-Agent | Multi-Agent Systems | Network Security | Observability | Prompt engineering | Responsible AI | Secure AI | Secure AI deployment | Shadow testing | Vector Search | Version control | Vertex AI
Education
N/A
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