AI Engineering Manager
CLP 9600K-10200K (estimate) Mid-level Full Time
Tasks
- Apply prompt engineering techniques
- Automate MLOps and LLMOps lifecycle
- Build and mentor AI engineering teams
- Build scalable inference infrastructure
- Conduct technical reviews and architecture assessments
- Create metrics and go no go gates
- Define AI system scope and limitations
- Design APIs microservices and orchestration layers
- Design and deliver RAG systems
- Design evaluation frameworks for LLM systems
- Establish model failure identification practices
- Implement CI/CD pipelines
- Implement LLM powered solutions for production
- Lead AI project delivery end to end
- Lead structured experiments for AI systems
- Manage AI model monitoring and retraining
- Run feasibility assessments
Perks/Benefits
- Company-provided equipment
- English lessons
- Flexible working options
- Learning opportunities
- Mentorship programs
- Travel opportunities
- Udemy Business
Skills/Tech-stack
APIs | AWS | Azure | CI/CD | Chunking | Cost Optimization | Dataset curation | Docker | Embeddings | Event Driven | Event-driven architecture | Experimentation | Few-Shot Learning | Few-shot | Fine Tuning | GCP | Git | Infrastructure as Code | Instruction design | Kubernetes | LLM Evaluation | LLMOps | Latency optimization | MLOps | MLflow | Machine Learning | Metric Design | Microservices | Precision at K | Prompt engineering | Python | RAG | Recall at K | Reliability Engineering | Reranking | Retrieval | Structured Output | Weights and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineering | AI Engineering Manager | Engineering | Engineering Manager | Manager
Regions
Countries
States
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