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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Engineer specializing in web apps and AI-powered tools
Harshit Vashisth
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
Rasheed Samady
Entry-level AI Engineer specializing in automation and ML platforms
“Built a production Python lead intelligence pipeline that combined external APIs, website crawling, and automated opportunity brief generation, with strong emphasis on reliability, observability, and recovery. Also has hands-on Playwright experience hardening flaky, dynamic web automations and reducing intermittent failures to under 5% through logging, screenshots, session management, and retry strategies.”
Sadir Ahmed Zidan
Junior AI/ML Engineer specializing in LLMs, RAG, and computer vision
“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”
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
Senior Machine Learning Engineer specializing in NLP and production ML systems
Mid-level Full-Stack AI Engineer specializing in RAG systems and intelligent automation
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.”
Anjali Chepuri
“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.”