Vetted Machine Learning Professionals

“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”

Sudarshan Naicker

Screened

Entry-Level Software Engineering Student specializing in Full-Stack Web Development and ML

Calgary, Canada1y exp

“Frontend-focused builder with multiple end-to-end academic product builds (event manager, niche social media app, airline booking site, AI-based obituary generator). Experienced in React/Next.js with an emphasis on scalable component architecture, pragmatic state management/performance, and strong collaboration with design via Figma; has shipped complex user flows like real-time chat, booking/seat selection, auth, and media upload under tight timelines.”

Bishara Bishara

Screened

Entry-Level Software Engineer specializing in AI/ML pipelines

1y exp

“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”

Felix Aldi I Simanjuntak

Screened

Intern QA / Junior Web Developer specializing in manual testing and Laravel

Jakarta Pusat, Indonesia0y exp

“Console-focused QA tester with hands-on PlayStation (PS4/PS5) functional/compliance testing experience, including TRC-driven validation, stability checks, controller input testing, and performance dip investigation in combat-heavy scenarios. Also tested a Nintendo casual puzzle-adventure title and uses AI assistants to speed up crash/log analysis and produce clearer test cases and bug reports.”

Tanushree Kakad

Screened

Intern Software Engineer specializing in AI, computer vision, and VR

Remote0y exp

“Robotics software engineer with hands-on experience integrating vision-based perception with control logic in ROS simulation environments. Focused on debugging real-time timing/data-flow issues, improving system stability through incremental scenario testing in Gazebo, and supporting reliable deployments with Docker and basic CI/CD automation.”

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”

“Built an automated ML/NLP document classification system for unstructured legal documents, combining classical models (TF-IDF + logistic regression/random forest) with entity resolution via fuzzy matching validated by precision/recall. Also implemented semantic similarity search using sentence embeddings stored in FAISS and improved matching by fine-tuning a transformer on domain-specific data and tuning similarity thresholds for fewer false positives.”