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Vetted Flask Professionals
Pre-screened and vetted.
Staff Software Engineer specializing in cloud-native healthcare and payments platforms
Mid-level Data Scientist specializing in Generative AI and LLM applications
Senior AI/ML Engineer specializing in generative AI and recommendation systems
Junior Software Engineer specializing in backend systems and security
Senior Software Engineer specializing in backend systems and data platforms
Mid-level AI/ML Engineer specializing in Generative AI and multilingual NLP
Senior Backend Developer specializing in cloud APIs and microservices
Senior Full-Stack Engineer specializing in React, Python/Django, and AWS
Andrew Stewart
Senior Software Engineer specializing in Healthcare AI and FinTech platforms
“Google Health engineer who owned and shipped an AI-powered clinical insights dashboard and NLP clinical note extraction service end-to-end (React/Next.js frontend; Python/Node microservices on GKE; TensorFlow transformers; BigQuery analytics). Demonstrated strong production rigor (CI/CD, testing, observability, guardrails for sensitive data) and delivered measurable outcomes including 30% faster diagnostics, 40% less manual documentation, 15% higher adoption, and 25% lower ops costs.”
Senior Full-Stack AI Engineer specializing in generative AI and cloud platforms
Senior Full-Stack Software Engineer specializing in cloud, payments, and telehealth
Senior Full-Stack Engineer specializing in microservices, data pipelines, and cloud platforms
Staff Software Engineer specializing in cloud-native healthcare and payments platforms
Senior Software Engineer specializing in distributed systems and cloud infrastructure
Senior Software Engineer specializing in cloud-native microservices and large-scale backend systems
Senior Full-Stack AI Engineer specializing in LLM/RAG and production ML platforms
Denis Siminiuc
Junior Research Assistant specializing in LLMs, NLP, and data systems
“Software-focused candidate who built a data monitoring pipeline during a hedge fund internship, integrating real databases and an email API to notify teams when data was ready. Comfortable working through legacy/scrappy code and uses LLMs to accelerate comprehension and delivery, with an emphasis on thorough testing and clear communication with stakeholders/customers.”
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”