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Vetted Observability Professionals
Pre-screened and vetted.
Mid-level Full-Stack Software Engineer specializing in Ruby on Rails and JavaScript SPAs
Mid-level Backend Software Engineer specializing in AI-powered microservices and cloud infrastructure
Senior Implementation & Solutions Engineer specializing in healthcare IT deployments
Mid-Level Software Engineer specializing in cloud-native full-stack and DevOps
Mid-level Full-Stack AI Engineer specializing in agentic RAG and LLM fine-tuning
Director-level engineering leader specializing in platform modernization and cloud architecture
Senior Software Engineer specializing in FinTech and treasury platforms
Senior Software Engineer specializing in cloud-native platforms and FinTech
Intern Software Engineer specializing in backend systems and machine learning
Senior Full-Stack Engineer specializing in frontend architecture and scalable web platforms
Senior Full-Stack Engineer specializing in React, Python, and AI-driven SaaS
Senior Software Engineer specializing in FinTech and digital banking
Senior Python Backend Engineer specializing in scalable APIs, cloud microservices, and AI/ML platforms
Senior DevOps/Cloud Engineer specializing in AWS infrastructure and CI/CD automation
Balaji Vellaluru
Mid-level Software Engineer specializing in full-stack and computer vision systems
“Built D.O.C.T.O.R, a 0-to-1 anatomy learning platform with 450+ 3D models that reached 6,500+ users, saved $850K versus a costly alternative, and was featured in a University of Illinois Chicago news article. The candidate combines product initiative with hands-on full-stack execution, spanning React/Three.js, databases, auth, analytics, and AI workflow automation side projects.”
Maneesh Ujji
Junior Machine Learning & Data Science professional specializing in AI agents and applied ML
“IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.”
Snigdha Reddy Podduturi
Junior Data & AI Engineer specializing in cloud AI and analytics
“Built production AI backend systems in healthcare and e-commerce, including a healthcare agent that automated clinical workflows like medication refills, immunizations, and scheduling using FHIR APIs and cloud-native infrastructure. Strong in end-to-end backend ownership, LLM orchestration, and adding guardrails/validation for high-stakes and customer-facing AI workflows.”
Michael Rosario
Senior Backend Engineer specializing in logistics platforms and API integrations
“Backend engineer focused on reliability and production operations, with hands-on experience implementing an outbox pattern to keep database updates and downstream events consistent. They also described diagnosing transaction-related latency under heavy client traffic and using monitoring data to validate performance improvements, showing a pragmatic, systems-oriented approach to scaling integrations.”
Phat Dang
Junior Software Engineer specializing in full-stack, DevOps, and GenAI
“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”
Saivedant Hava
Entry AI Engineer specializing in LLMs, RAG, and MLOps
“Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.”