LLM, agents & NLP

QA Engineer experienced in manual testing and web application validation, including functional, regression, smoke, sanity, and UI checks. Hands-on experience validating REST APIs with Postman and automating web workflows with Playwright, plus defect documentation and retesting support. Also completed an AI-focused internship involving AI workflow testing and collaboration with developers.

Frontend developer focused on building polished static portfolio and marketing websites at a junior level with a strength for visual UI and theme-driven pages. The strongest proven skill is HTML/CSS layout and visual implementation as seen in the portfolio pages and the recipe site (single-recipe.html and associated CSS). There is little evidence of application engineering depth such as component systems, async state management, server integration, automated testing, or measured performance work.

Backend Node.js engineer at a middle level focused on building REST APIs and validation-heavy server logic. The strongest proven skill is robust input validation and test coverage as shown by backend/src/validation/ticketValidation.js and the corresponding unit and integration tests in backend/src/validation/__tests__/ticketValidation.test.js and backend/tests/integration/*.test.js. There is limited public evidence of advanced distributed systems design, schema migrations, production-grade observability or explicit retry/backoff and circuit breaker patterns.

Backend API engineer (Senior level) focused on asynchronous realtime systems and agent tooling. The strongest proven skill is building end-to-end realtime messaging and WebRTC/WebSocket plumbing, demonstrated by app/services/websocket_service/ws_endpoint.py and frontend call_session.py plus audio_manager.py. There is limited public evidence of production-grade infra like CI/CD, observability/metrics, and a documented DB migration history with evolution scripts.

Real-time pose-detection and applied ML developer (Middle level) focusing on Streamlit-based computer vision apps with modular landmark-based detectors. The strongest proven skill is integrating a MediaPipe pose landmarker into a real-time processing loop and building custom landmark heuristics for exercise metrics, as shown in services/vision/exercise_video_processor.py and detectors/biceps_curl.py. There is limited evidence of custom model training pipelines, experiment tracking, production MLOps, or automated test coverage in public code.

Aspiring AI Engineer with hands-on experience in Generative AI, Large Language Models (LLMs), Machine Learning, and MLOps. Skilled in building RAG applications, AI agents, and scalable ML deployment pipelines using Python, LangChain, Docker, Kubernetes, and AWS. Seeking to leverage technical expertise to develop innovative AI-driven solutions and contribute to impactful business outcomes.

Backend-focused engineer (senior-level) with a strength for local-first, data-oriented tooling and demonstrable attention to data integrity and safety. The strongest proven skill is building a local synthetic scenario quality platform with careful schema migrations, signed replay artifacts, a safe SQLite job queue and benchmarking (see scenario_quality/engineering.py and scenario_quality/advanced.py). The public artifacts do not show production-grade service deployments, cloud infrastructure, or large distributed systems patterns such as durable, horizontally scaled queues or multi-service contracts.

Trust & Safety professional with 5+ years of experience in high-volume content moderation and quality assurance in technology-platform operations. Experienced in content policy enforcement, risk assessment and escalation, quality audits, and KPI/SLA monitoring, with analytical work supported by Microsoft Excel.

AI governance and algorithm auditing professional with ISO/IEC 42001 and ISO 27001 implementation experience, including AI policy and risk management for managed certification activities. Also works as an AI engineer building document intelligence and conversational AI agents, with a focus on practical deployment and system transparency.

An experienced ML practitioner (senior-level notebook work) focused on entity resolution and duplicate detection with strong feature engineering and evaluation skills. The single strongest proven skill is production-minded entity-resolution feature engineering and pairwise modeling as demonstrated in AI-customer-deduplication/EDA_and_model.ipynb (profile-level aggregation, fuzzy/email/phone normalization, leakage-aware pair generation, and graph-based clustering). There is limited evidence of production engineering around testing, CI/CD, scalable candidate-generation (blocking at scale), and model serving/monitoring in public artifacts.

Senior full-stack engineer focused on enterprise modernization and production support. Experience spans C#/.NET backend and REST APIs with Angular/TypeScript frontends, plus document automation and migration of legacy Perl/Catalyst and LaTeX functionality. Has used AI-assisted development tooling including GitHub Copilot and MCP workflows for coding, review, QA/testing, and legacy modernization.

Frontend UI engineer (middle level) focused on polished component work and visual design tokens. The strongest proven skill is building UI primitives and local state patterns, exemplified by the custom global toast system in src/hooks/use-toast.ts and a consistent tokenized theme in src/index.css and tailwind.config.ts. There is limited evidence of formal testing, large-scale architecture decisions, advanced server-state handling or explicit accessibility implementations in public code.

Front-end UI developer (early career, junior) focused on building static, well-structured e-commerce pages with solid visual polish. The strongest proven skill is HTML and layout design, demonstrated by the product listing and product detail markup in index.html and pages/produtos/product.html. There is limited evidence of JavaScript architecture, state management, automated testing, or measurable performance work in the human-authored codebase.

Platform-focused backend engineer (senior) specializing in reliable, test-driven AI operational tooling and governed orchestration. Their strongest proven skill is designing deterministic, auditable orchestration and recovery semantics as shown by the exhaustive tests that pin the acquisition-floor and recovery-decision invariants (project-boundary-evidence/validation/test_acquisition_floor.py and test_recovery_decision.py). The public code has limited evidence of production-scale service orchestration patterns like distributed rate limiting, schema migration histories, or multi-service contract definitions that would prove cross-service system-design leadership.

Backend-focused developer (middle level) with a practical strength in building small-to-medium JVM-based data-backed applications. The strongest proven skill is database-driven application wiring and JDBC-based DAO implementation as shown in StudySync-Pro/src/main/java/com/studyplatform/dao/StudyPlanDAO.java which includes table creation, prepared statements, generated keys handling and transaction rollback. There is limited evidence of production-grade API versioning, auth flows, formal migration chains, caches, or distributed systems design in public code.

A backend-focused Java API engineer with production-style microservices experience at a senior-adjacent level and a strength for building REST APIs with transactional, security and realtime concerns. The strongest proven skill is building backend service logic and integrations, evidenced by ExecutionService (local/Judge0 execution, timeouts and cleanup) and multiple domain services with tests such as SubmitMusicRequestService and PlayerPlaybackService. The public code does not show operational infra like deployment manifests, secret management, measured performance tuning or long-lived migration histories.

Full Stack and AI engineer with experience building production backends, web platforms, and cloud infrastructure. Built multi-agent LLM workflows for an OCR-based healthcare solution and supported asynchronous processing for enterprise client environments. Also developed end-to-end features for a pediatric consultation platform, including backend services, integrations, and AWS-based deployments.

Product Manager with 5+ years in product management for enterprise IT products, with prior experience across software delivery and quality engineering. Has led AI-powered automation and self-healing initiatives for endpoint and IT operations, including self-service and compliance workflows, and delivered an infrastructure change management platform. Previously led QA strategy for web/mobile products and managed quality activities across healthcare technology projects.

ML engineer with end-to-end project experience at a senior level focusing on knowledge graph pipelines and embedding-based models. The strongest proven skill is knowledge-graph engineering and KGE training as shown by src/kge/train_eval.py and src/kg/expand.py which implement training, evaluation, SPARQL expansion and enrichment workflows. There is limited evidence of production MLOps, advanced efficiency engineering like quantization, and formal test coverage or deployment automation in public code.

I am an AI/ML Engineer with over one year of hands-on experience building Generative AI applications, Retrieval-Augmented Generation (RAG) pipelines, agentic AI systems, and scalable Python backend services. At VectorStack, I have worked with Python, FastAPI, LangChain, LLMs, vector databases, MongoDB, and PostgreSQL to develop production-oriented AI solutions, including document question-answering systems and an F1 racing chatbot. I enjoy solving complex problems, learning emerging technologies, and transforming AI concepts into practical applications. With a Master of Computer Applications (MCA) degree, I am looking for opportunities to contribute to innovative AI products, strengthen my engineering expertise, and build reliable, scalable AI systems that deliver measurable business value.