Vetted Jenkins Professionals

“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”

“Backend-focused engineer with experience spanning research and healthcare: owned a Python/SQL data pipeline that transformed vulnerability-fix code data from SQLite into model-ready JSON for LLM analysis. Also deployed Dockerized Spring Boot microservices to Kubernetes with Jenkins CI/CD and built Kafka-based real-time event streaming (appointment/report events) with idempotent consumers to avoid duplicate processing.”

“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”

“Backend/AI engineer with production experience in legal tech: built a high-scale licensing/subscription API (FastAPI/Postgres/Stripe) and shipped a RAG-based chatbot for an eDiscovery platform. Designed a robust legal document ingestion workflow that processes thousands of documents into a searchable vector index with clear retry/escalation logic, and has demonstrated measurable Postgres performance wins (200ms to 10ms) using EXPLAIN ANALYZE and composite indexing.”

Kevin Thomas

Screened

Intern Software Engineer specializing in AI, cloud, and backend systems

Torrance, CA1y exp

“Candidate has internship and graduate-project experience building AI agents, including a production log-analysis assistant using a lightweight agentic/RAG-style workflow with local GPT training and validation against historical logs. They also worked on Android/iOS game build and release processes in a Unity-based robot racing game environment, and highlight measurable LLM outcomes including 80% analysis accuracy, 2-5 second latency, and 50% cost reduction.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“Embedded robotics/industrial automation engineer focused on Cortex-M + FreeRTOS modules for robotic assembly lines, owning low-level drivers (SPI/UART/I2C), deterministic timing, and high-reliability comms. Strong in hardware/software integration (board bring-up, timing debug with scope/logic analyzer/JTAG) and production-grade firmware delivery via Jenkins CI and Docker; has supported ROS systems via rosserial and C++ nodes to publish clean sensor data.”

srikanth tulluru

Screened

Mid-level Product Designer & Design Technologist specializing in design systems and GenAI UX

Remote, USA3y exp

“Enterprise/industrial UX designer focused on making complex, real-time automated systems feel trustworthy and predictable. Has hands-on experience observing operators in logistics/automation environments, building shared interaction models to unify fragmented products, and collaborating tightly with engineers using component-system thinking (HTML/CSS/TypeScript) to ship resilient UIs that handle partial failures.”

Prasanth Kumar Korada

Screened

Mid-level QA Automation Engineer specializing in Playwright and cross-browser E2E testing

3y exp

“QA automation engineer with strong end-to-end ownership of UI automation for financial transaction workflows, using Selenium/Playwright/Cypress and CI/CD gating. Improved suite robustness via UI-API validations, negative testing, and flake reduction (intercepts + data-testid), catching critical backend calculation issues before production and cutting regression runtime by 40%.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”