Turning AI capabilities into production-ready products for real business workflows.

Digital product consultancy delivering software engineering, design, and AI solutions for startups and enterprises.

• Architected and delivered LLM-powered workflow systems that transformed unstructured inputs into structured, actionable outputs, reducing manual processing effort by 30-40% across high-volume operational flows.
• Built Python-based RAG pipelines integrating curated knowledge sources, retrieval, and prompt-grounding logic, improving factual accuracy and reducing unsupported or hallucinated responses by 30%.
• Developed agent-style orchestration workflows for multistep reasoning, task routing, and exception handling, increasing successful end-to-end workflow completion rates by 25% without human intervention.
• Designed and exposed model-backed APIs enabling real-time AI capabilities across internal and customer-facing products, achieving sub-300ms median latency and supporting high-throughput usage scenarios.
• Implemented full-stack AI product features using React and TypeScript, including workflow dashboards and review interfaces, reducing manual review time by 25% and improving user adoption across internal teams.
• Integrated frontend, backend, and AI inference layers into cohesive production systems, enabling seamless AI-driven user experiences and reducing system friction across workflows.
• Introduced evaluation pipelines for retrieval quality, grounding accuracy, and response reliability, reducing model iteration and validation cycles by 30-35%.
• Deployed and scaled containerized AI services on AWS (ECS, S3, CI/CD pipelines), improving platform availability to 99.9% and reducing deployment time by 40-50%.

Cloud-native AI document automation company building end-to-end workflow automation for transactional business processes.

• Built Python services for AI-driven document and transaction workflows, reducing manual processing time by 45% across high-volume business operations.
• Developed AI agent-style orchestration logic to coordinate document ingestion, validation, and exception handling workflows, enabling adaptive decision-making across automated processing pipelines.
• Applied PyTorch and scikit-learn to improve document classification, field extraction, anomaly detection, and exception routing within automated workflow pipelines.
• Designed and implemented workflow orchestration logic for validation, approval, and post-processing flows, improving end-to-end workflow completion speed by 30% and reducing manual intervention in document processing.
• Delivered model-backed capabilities through REST APIs and internal service endpoints, allowing workflow engines and external systems to consume predictions in near real time.
• Deployed production-grade ML inference services using Docker and AWS (ECS, S3), enabling scalable model serving and reducing deployment time by 50% through automated CI/CD workflows.
• Designed workflow automation using n8n to orchestrate AI-powered document processing pipelines, integrating model outputs with downstream systems through APIs and webhooks.

Language AI company building machine-learning-powered translation, writing, and communication products.

• Engineered Python-based NLP pipelines for language processing tasks, improving the quality and consistency of text understanding workflows used in production translation services.
• Trained and fine-tuned neural models with PyTorch and TensorFlow for machine translation quality estimation, language detection, and text classification, improving offline model performance by 15%.
• Applied scikit-learn to prototype baseline models, feature extraction workflows, and offline evaluation utilities that accelerated experimentation across NLP projects.
• Orchestrated data preparation and feature generation jobs for large multilingual corpora, increasing dataset reliability and reducing preprocessing failures by 25%.
• Operationalized inference services with containerized deployment workflows, helping move ML functionality from experimentation into stable internal and customer-facing systems.
• Refined model evaluation and error analysis processes for multilingual outputs, raising confidence in production releases and shortening iteration cycles for new model versions.

Global enterprise software company building data, analytics, and intelligent business platforms.

• Developed a full-stack enterprise application using React, TypeScript, and Node.js, delivering scalable features for data-driven business workflows.
• Built reusable UI components and dashboards to support analytics and intelligent automation scenarios, improving usability and feature delivery speed.
• Implemented backend services and RESTful APIs to process and expose structured business data across distributed systems.
• Integrated frontend applications with backend and data services to enable near real-time data visualization and operational insights.
• Contributed to features aligned with automation and machine learning-assisted workflows, enhancing system capabilities for intelligent decision support.
• Applied TypeScript, OOP principles, and modular architecture to improve code maintainability and scalability across shared components.
• Collaborated with cross-functional teams in an agile environment to deliver end-to-end functionality across UI, API, and data layers.
• Enhanced application performance by optimizing API response handling and frontend rendering efficiency, reducing page load times by 23%.

A leading game development company building large-scale, real-time mobile gaming platforms.

• Developed gameplay systems and backend services using Java (Spring Boot) with strong object-oriented programming principles, improving modularity and maintainability of game logic.
• Designed and implemented scalable services for player data, game events, and session management, supporting high-concurrency gameplay environments.
• Built internal tools and dashboards using JavaScript (React) for game configuration, monitoring, and analytics, reducing manual setup and configuration effort by 30% and improving visibility into live game operations.
• Modeled game mechanics and event systems using OOP design patterns, enabling flexible feature extensions and shortening new feature rollout cycles by 20%.
• Integrated RESTful APIs and messaging systems to support real-time game interactions and event-driven architecture, improving system responsiveness and ensuring stable handling of high-concurrency player activity.
• Optimized performance of backend services by improving data access patterns and reducing response latency.
• Investigated and resolved production issues, delivering timely hotfixes that reduced recurring live-service incidents by 30% and improved system stability during peak usage periods.