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Vetted Bash Professionals
Pre-screened and vetted.
Senior Multi-Cloud DevOps/SRE Engineer specializing in Azure, AWS and IaC
Mid-level Data Scientist specializing in customer analytics, ML pipelines, and churn forecasting
Senior Software Developer specializing in cloud-native microservices and data platforms
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and cloud microservices
Intern Machine Learning Engineer specializing in recommender systems and financial risk modeling
Junior Full-Stack Software Engineer specializing in Real Estate SaaS and AI automation
Mid-Level Full-Stack Software Engineer specializing in FinTech and AI risk scoring
Senior Full-Stack & AI/ML Engineer specializing in cloud-native platforms and LLM systems
Mid-level Full-Stack Developer specializing in FinTech and fraud detection
Intern Software Engineer specializing in cloud governance and distributed systems
Junior Multimodal AI & Systems Engineer specializing in robotics and cloud infrastructure
Junior Cybersecurity Analyst specializing in SOC monitoring, SIEM detection, and endpoint security
Mid-Level Software Engineer specializing in cloud-native distributed systems
Mid-level AI/ML Engineer and Developer Educator specializing in GenAI, RAG, and AI community building
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Principal DevOps/SRE Engineer specializing in multi-cloud infrastructure and DevSecOps
Senior Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
Intern Software Engineer specializing in systems programming and game development
Senior DevOps / Cloud / Site Reliability Engineer specializing in AWS and Kubernetes
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.”