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Vetted Kubernetes Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in data platforms and FinTech/SaaS systems
Director of Engineering specializing in AI/ML, data platforms, and consumer messaging
Executive product and engineering leader specializing in FinTech and healthcare technology
Senior Full-Stack Engineer specializing in Java microservices and FinTech
Senior Software Engineer specializing in full-stack web development
Senior Software Engineer specializing in full-stack SaaS and compliance platforms
Mid-level Software Developer specializing in backend cloud and API platforms
Mid-level Software Engineer specializing in Applied AI and FinTech
Mid-level Software Engineer specializing in AI systems and FinTech
Junior Software Engineer specializing in backend, cloud, and AI-enabled enterprise systems
Executive CTO/VP Engineering specializing in high-performance AI, data systems, and distributed infrastructure
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Executive Engineering Leader specializing in Telehealth Platforms and Healthcare IT
Mid-level Linux DevOps Engineer specializing in automation and cloud infrastructure
Senior Software Engineer specializing in distributed systems, IoT, payments, and blockchain
Senior AI/ML Engineer specializing in Generative AI and cloud-native ML systems
Rubeena Riyas
Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms
“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”