Ronald Forte

About

Junior/entry-level AI/LLM engineer who built a production-oriented RAG onboarding and knowledge assistant that ingests GitHub repos and internal sources (e.g., Confluence/Jira) using ChromaDB, with reliability features like retrieval fallbacks, retries, caching, and monitoring. Currently implementing a LangGraph-based multi-agent workflow with intent routing and Pydantic/Magentic-validated structured outputs, plus CI/CD offline evals and online metrics (Grafana/Prometheus) to improve predictability and reliability.

Key Strengths

• Shipped RAG-based onboarding/knowledge assistant for engineering codebases and internal docs
• Production hardening for LLM systems (fallbacks for weak retrieval, retries/timeouts, caching, context-window control)
• Agent orchestration design with intent-based routing and multi-agent decomposition (LangGraph)
• Type-safe/validated structured outputs to improve predictability (Pydantic/Magentic)
• Evaluation strategy spanning offline regression tests in CI/CD plus online monitoring and error categorization
• Data cleaning/standardization approach for messy operational/ERP-like datasets (Python/pandas) with permission-aware fallbacks and idempotency

Reference Highlights

• Strong end-to-end agent workflow architect
• Designs modular, extensible systems
• Careful attention to correctness and reliability
• Methodical approach to quality improvement
• Effective error analysis and iterative improvement
• Improved response accuracy and reliability through validation and prompt changes
• Handles messy/ambiguous operational data effectively
• Builds robust integrations with safe fallbacks and validations
• Strong collaboration skills on large teams
• Clear client communication of technical work
• Learns new things quickly
• Insight was highly appreciated by the team
• Delivered a RAG onboarding/knowledge assistant with positive client feedback
• Collaborative idea generation (bounced ideas with partner and reference)
• Creative, thinks outside the box
• Proactive in adding functionalities beyond what was shown
• Focus on real-world problem solving
• Strong commitment to learning (scheduled lots of time to get up to speed)
• Able to grasp new concepts quickly (things clicked immediately)
• Improved ML model performance significantly via data cleaning/prep
• Understands orchestration/agentic workflows despite learning curve
• Good prior understanding of APIs and data contracts
• Thoughtful about robustness (checks and safe fallbacks)

Experience

Education

Skills

Languages