.png){kind=logo}
Vetted Audit Logging Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in Python backends and cloud-native systems
Senior QA Analyst specializing in Agile testing for healthcare and public sector systems
Junior Software Engineer specializing in full-stack web and applied ML
Mid-level Full-Stack AI Engineer specializing in LLM automation and scalable APIs
Mid-level ML Engineer specializing in FinTech risk, fraud, and GenAI RAG systems
Mid-Level Software Engineer specializing in full-stack web and backend systems
Mid-level GenAI Engineer specializing in agentic workflows, RAG, and LLM orchestration
Mid-level Data Engineer specializing in AI, analytics, and cloud data platforms
Staff Software Engineer specializing in Python APIs and AWS-native data platforms
Mid-level Backend Software Engineer specializing in cloud microservices and AI agent systems
Mid-Level Full-Stack Software Engineer specializing in cloud microservices and mobile apps
Mid-level Full-Stack Engineer specializing in AI products and FinTech
Senior Full-Stack Python Engineer specializing in secure cloud platforms and ML systems
Ruthvika Mamidyala
Mid-level Data Scientist specializing in GenAI, RAG, and predictive modeling
“Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.”
Kruti Mehta
Mid-level Software Engineer specializing in backend systems and FinTech analytics
“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”
Pranava Reddy Kothapally
Junior Data Engineer specializing in Azure, CRM data pipelines, and marketing personalization
“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”
Shaktisharan Rajure
Mid-level Full-Stack Engineer specializing in backend APIs on AWS (Healthcare & FinTech)
“Backend engineer who evolved and migrated a real-time smartwatch telemetry ingestion/analytics platform in a healthcare context, focusing on reliability under poor network conditions. Experienced with Python/FastAPI and Java microservices, PostgreSQL performance tuning, and production-grade security (JWT/OAuth, RBAC, RLS) with incremental rollout and parallel-run migration strategies.”
Karan Savaliya
Junior Backend Engineer specializing in data platforms and cloud APIs
“Backend lead at a stealth startup and builder of MailIQ/MailBox—an automated Gmail inbox digest + cleanup system. Designed secure multi-account email ingestion and cost-efficient LLM-based summarization, and implemented robust unsubscribe automation using Playwright + OpenAI webpage analysis (including captcha-handling) with strong safety guardrails, incremental rollouts, and rollback strategies.”
Vengalarao Pachava
Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems
“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”
Monish Reddy Bonthu
Mid-Level Full-Stack Engineer specializing in real-time systems and FinTech
“Backend engineer with hands-on experience modernizing a real-time logistics/tracking platform from a tightly coupled polling architecture to a service-oriented/microservices design using Node.js and WebSockets. Emphasizes contract-first FastAPI development, defense-in-depth security (JWT/OAuth, RLS/Supabase), and safe incremental migrations with feature flags and strong observability, delivering sub-second updates and improved performance under peak load.”
“Built a production ad-spend optimization system that combined deterministic audit logic with LLM-generated explanations, surfacing severe inefficiencies including 70-90% wasted spend in some Google Ads accounts. Stands out for pairing measurable business impact with pragmatic AI safety and usability decisions, including approval-gated execution and structured, human-readable recommendations.”