Pre-screened and vetted.
Mid-Level Full-Stack Software Engineer specializing in web apps and AI-powered tools
Junior AI Engineer specializing in LLMs, RAG systems, and MLOps
“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”
Junior AI/ML Researcher specializing in deep learning, computer vision, and LLM applications
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Entry-Level Full-Stack AI Engineer specializing in RAG pipelines and enterprise SaaS
Senior Full-Stack & AI Engineer specializing in scalable web and cloud applications
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
Junior Computer Vision Engineer specializing in generative AI and autonomous perception
“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 an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”