Vetted Software Engineers

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”

“Backend/data engineer focused on climate/emissions data platforms, building production Python (FastAPI) microservices and AWS serverless/ETL pipelines (Glue/Athena/Lambda/EventBridge). Demonstrated strong reliability and observability practices plus measurable optimization wins, including cutting PostgreSQL query runtimes from minutes to seconds and reducing AWS costs from ~$6k/month to ~$400/month.”

“Unity game developer who implemented a swipe-based pack-slicing minigame in "Herois da Fruta," improving responsiveness by evaluating both swipe start and end positions. Has multiplayer AR experience on "ARcade Sports" using Photon Unity Network to sync four AR devices, including coordinating AR anchors with online communication and camera/world alignment. Prefers collaborative teams and a 9-to-5 schedule; targets €17k–€20k depending on responsibilities and benefits.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”

“Built a voice-driven desktop assistant for users with mobility impairments, integrating Whisper and Google Gemini and adding voice-authentication via speaker embeddings for secure command execution. Has hands-on experience with AWS serverless/microservices patterns (Lambda, S3, CloudFront, CloudWatch) and CI/CD, plus built an internal MySQL-to-MongoDB migration tool used by the CTO and dev team with an emphasis on safe, low-impact data transformation.”

“Backend-focused developer who built a Python 4v4 matchmaking system using win/loss history plus an Elo rating model, validating and tuning it against a dataset of ~50 real games. Previously worked at Advanced Logistics Management building time-sensitive agriculture-site modules in a small dev team, coordinating work via Jira/Git and drafting documentation for a potential migration from XAMPP/CodeIgniter/Bluehost to Azure.”

“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”

“Security-focused engineer with hands-on experience implementing and troubleshooting security tooling (including an open-source SIEM) and integrating SCA/container scanning into AWS/EKS and GitHub Actions pipelines. Demonstrates strong cloud security fundamentals (least-privilege IAM, IRSA, private subnet/VPC design, CloudTrail/GuardDuty) and can translate security-usability tradeoffs (e.g., password policy and 2FA) to different stakeholders.”

“Python/Django backend engineer with open-source experience upgrading Archivematica to Django 4.2 LTS, including resolving a tricky breaking change in datetime parsing by implementing a preservation-safe legacy timestamp conversion layer. Also built a cost-efficient, reproducible Small Language Model (Microsoft Phi-3) fine-tuning pipeline that turns CSV product data into a domain-specific searchable Q&A chatbot, with emphasis on memory optimization and overfitting prevention.”

“Full-stack system analyst/programmer at PeakPlay Sports (startup) who built an AI "coach" product end-to-end in ~2 months, using a LangGraph-orchestrated multi-agent architecture with a FastAPI backend. Shipped production RAG grounded in athlete history (OpenAI embeddings + vector store) with guardrails and a structured eval loop (golden set + LLM-judge + human review) to improve engagement and reduce hallucinations.”

“Frontend engineer focused on scalable React + TypeScript dashboards and workflows, using feature-based architecture and a reusable component/hook approach. Has hands-on experience improving performance in table-heavy admin UIs (pagination, lazy loading) and shipping major features quickly with staging/manual QA, gradual rollouts, and rapid iteration based on production feedback.”

“AI/LLM engineer with production experience delivering large-scale RAG and voice-agent solutions for banking clients. Implemented a SharePoint-based, non-technical content update workflow with incremental hourly ingestion into a vector DB, and actively contributes to Microsoft’s open-source GPT-RAG accelerator while using modern orchestration (Semantic Kernel, LangGraph) and LLM observability/evaluation tooling.”

“Full-stack engineer at Srasys Inc. who built and owned production payments/checkout for an e-learning platform serving 5,000+ users using Next.js App Router + TypeScript. Deep focus on correctness and reliability (Stripe webhooks, signature validation, DB-level idempotency) plus measurable performance wins (~40% latency reductions) through Postgres indexing/EXPLAIN ANALYZE and Redis-backed caching with CloudWatch monitoring.”

“Built and shipped production AI features in the automotive dealership domain, including an end-to-end computer-vision damage detection system for trade-ins and a tool-calling, RAG-enabled LotSync AI Agent that answers inventory/VIN questions using strict schemas and internal APIs to avoid hallucinations. Also developed a Dagster + Oracle automated reporting pipeline as a Graduate Research Assistant, supporting 15+ university departments with normalized, reliable ETL workflows.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”

“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”

“Backend engineer at Hexanika who owned a real-time fraud-detection platform: built Django microservices, integrated a GenAI anomaly-scoring model, and optimized data/infra for low-latency production (including ~40% query-latency reduction). Experienced running containerized services on AWS/GCP with Kubernetes/GKE, GitHub Actions-based CI/CD + GitOps, and building Pub/Sub streaming pipelines and on-prem-to-cloud migrations.”

“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”

“Backend engineer who built and scaled a FastAPI-based backend for an AI-driven maintenance system automating vendor sourcing/bidding/communication. Emphasizes async, message-driven architecture with strong observability and state-machine-driven workflows, plus robust webhook/idempotency patterns to prevent duplicate/out-of-order events from causing bad bids or state changes.”

“Backend engineer building an AI-powered social media platform on AWS, with hands-on experience shipping LLM-backed application features and improving production performance under high traffic. Strong focus on reliability/observability (CloudWatch, structured logs, health checks) and database optimization (MongoDB explain/slow logs, indexing, caching, connection pooling).”

“Full-stack engineer with deep startup experience (pre-seed through IPO/SPAC) currently building a Next.js/TypeScript SaaS sports analytics platform with a complex Postgres-based entitlement/ACL system. Has delivered measurable UX/business impact (35% retention lift, 40% volume increase) and built production-grade daily ETL + model training/inference workflows with validation and checkpointing for reliability.”