AI Engineering Lead
Santiago, Santiago Metropolitan Region, Chile
CLP 49980K-58800K (estimate) Senior-level Full Time
Tasks
- Automate MLOps and LLMOps lifecycle
- Build agentic frameworks
- Build inference infrastructure
- Conduct AI feasibility assessments
- Create prompt engineering strategies
- Design AI APIs and microservices
- Design and build RAG systems
- Design evaluation frameworks
- Develop LLM powered production solutions
- Identify model failure modes
- Implement CI/CD pipelines for AI models
- Implement orchestration layers for latency cost and reliability
- Lead end-to-end AI project delivery
- Mentor junior engineers
- Run structured prompt and model experiments
Perks/Benefits
- Company-provided equipment
- English lessons
- Flexible working options
- Learning opportunities
- Mentorship programs
- Special day rewards
- Travel to industry conferences
- Udemy Business
Skills/Tech-stack
API Design | AWS | Azure | CI/CD | Containerization | Databricks | Databricks MLOps | Embeddings | Evaluation | Event Driven | Event-driven architecture | Few-Shot Learning | Few-shot | GCP | Git | Inference Optimization | Information Retrieval | Infrastructure as Code | Kubernetes | LLMOps | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Microservices | Observability | Prompt engineering | Python | Reranking | Retrieval-Augmented Generation | Security | Snowflake | Weights and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineering | AI Engineering Lead | Engineer | Engineering Lead | Lead | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
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