Vetted LoRA Professionals

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”

“Robotics/embedded candidate who built an end-of-studies restaurant waiter robot using Raspberry Pi 5 + ROS 2 Jazzy with Arduino Mega motor control and an ESP32-based table call button system. Implemented sensor nodes (LiDAR/ultrasonic/IMU), SLAM mapping with slam_toolbox, and navigation with Nav2; overcame major odometry calibration issues using EKF filtering and validated behavior in Gazebo before deploying to the real robot.”

“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”

“Master’s thesis engineer who built and deployed a continuous real-time perception + state estimation + control loop under tight latency constraints, owning both software architecture and hardware integration. Strong ROS 2 fundamentals with a systems-first approach—stabilizes robotic behavior by instrumenting, logging/replaying real data, and fixing timing/synchronization issues rather than treating failures as purely algorithmic.”

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”