Vetted UNIX Professionals

Sai Haritha Sardena

Screened ReferencesStrong rec.

Mid-level Full-Stack Software Engineer specializing in web, cloud, and AI/ML

Remote, USA7y exp

“Software engineer with experience at Wipro and Tylmen Tech owning customer-facing onboarding and real-time features end-to-end (React + Spring Boot) and building TypeScript/React apps backed by Node.js microservices (MongoDB, RabbitMQ). Strong in production reliability and fast iteration: feature-flagged rollouts, idempotent APIs/consumers, DLQs, and SLO-driven incident tooling, including an internal QA & release dashboard adopted by engineering and support teams.”

Vigneshwari Shankiri

Screened ReferencesStrong rec.

Senior Java Full-Stack Developer specializing in microservices and cloud platforms

5y exp

“Frontend engineer who has led enterprise-scale UI delivery end-to-end on a microservices platform, designing modular Angular SPAs (v12-17) tightly aligned to Spring Boot REST APIs. Emphasizes quality and release velocity through layered testing (Karma/Jasmine), CI/CD automation (Jenkins/Azure DevOps), performance tuning with RxJS/lazy loading, and incremental rollouts with close product/design/QA collaboration.”

“Full-stack product engineer who owned a live-events digital ticketing platform end-to-end, including blockchain-based ticket validation and high-traffic booking flows. Stands out for combining Angular/React frontend work with Java/Spring Boot backend architecture, plus strong production reliability practices around concurrency control, queues, observability, and UX optimization.”

“AI engineer who built a production e-commerce system that analyzes product images alongside sales and demographic data to generate actionable creative recommendations, now used by 20+ clients. Also built orchestrated document/agent pipelines (Airflow, LangGraph) including a compliance drift detector auditing 401 compliance documents, with an emphasis on traceability, logging, and production integration.”

“Master’s student and Deep Learning teaching assistant who teaches LLM/VLM fine-tuning (including LoRA) and built a Hugging Face LLM fine-tuned for unit conversion, improving reliability by analyzing synthetic data and filling missing number-system conversion examples. Also implemented the Raft consensus protocol using gRPC in a distributed systems course with correctness validated by unit tests.”

“Deloitte engineer who built and shipped AI-powered, Kafka-driven workflow automation for transportation/document processing, including LLM-based semantic search. Strong in production reliability (idempotency, offset management, retries), observability (Datadog/CloudWatch), and database performance tuning (PostgreSQL/Flyway), with measurable latency improvements.”