AI Engineering Lead
Guadalajara, Jal., Mexico
MXN 900K-1100K (estimate) Senior-level Full Time
Tasks
- Analyze model failure modes
- Apply prompt engineering techniques
- Automate MLOps and LLMOps lifecycle
- Build CI CD pipelines for AI models
- Build inference infrastructure
- Conduct feasibility assessments
- Create evaluation frameworks
- Define AI system scope and communicate risks
- Design APIs and microservices
- Design and build RAG systems
- Design and build agentic LLM solutions
- Implement orchestration layers for latency cost reliability
- Lead end-to-end AI project delivery
- Mentor junior engineers
- Run structured experiments
Perks/Benefits
- Company-provided equipment
- English lessons
- Flexible working options
- Learning opportunities
- Mentorship programs
- Paid leave
- Remote work bonus
- Travel opportunities
- Udemy Business
Skills/Tech-stack
AWS | Automation | Azure | CI/CD | Dataset curation | Deployment | Docker | Embeddings | Evaluation | Event Driven | Event-driven architecture | Experimentation | Few-Shot Learning | Few-shot | Fine Tuning | GCP | Git | Infrastructure as Code | Kubernetes | LLMOps | MLOps | MLflow | Microservices | Monitoring | Precision at K | Prompt engineering | Python | RAG | REST APIs | Recall at K | Reranking | Retrieval | Structured outputs | Tracking | Versioning | Weights and Biases | “as-code”
Education
N/A
Roles
AI | AI Engineering | AI Engineering Lead | Engineering Lead | Lead
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