I’m a Full-Stack Engineer and Mobile App Developer with strong expertise in AI/ML, SaaS platform development, and scalable cloud solutions. Over the years, I’ve built everything from intuitive mobile applications to enterprise-grade SaaS platforms, combining robust engineering with intelligent automation to deliver impactful results for clients. My strength lies in bridging the gap between machine learning innovation and end-to-end product development. Whether it’s designing multi-tenant SaaS platforms, building full-stack applications, or integrating AI into mobile apps, I ensure the final product is secure, scalable, and user-friendly. Core Skills & Expertise Full-Stack Development ✅ Frontend: React, Next.js, Angular, Vue.js ✅ Backend: Node.js, Express, Django, Flask, FastAPI, Spring Boot ✅ Databases: PostgreSQL, MySQL, MongoDB, Firebase, Redis ✅ API Development: RESTful APIs, GraphQL Mobile App Development ✅ Cross-Platform: Flutter, React Native, Ionic ✅ Native: Swift (iOS), Kotlin/Java (Android) ✅ UI/UX: Material Design, SwiftUI, Responsive UI AI & Machine Learning ✅ Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras ✅ Computer Vision & NLP: OpenCV, Hugging Face Transformers, spaCy ✅ Model Deployment: ONNX, TensorFlow Lite, ML model APIs ✅ Data: Pandas, NumPy, Matplotlib, data preprocessing & pipelines SaaS Platform Development ✅ Multi-tenant architecture, subscription management, and user roles ✅ Payment integrations (Stripe, PayPal, Braintree) ✅ Authentication & security (OAuth2, JWT, SSO) ✅ Analytics dashboards and reporting systems Cloud & DevOps ✅ AWS (EC2, S3, Lambda, RDS, SageMaker), Google Cloud, Azure ✅ CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) ✅ Docker & Kubernetes for containerized deployments ✅ Serverless architecture and microservices Other Tools & Skills ✅ Git/GitHub/GitLab, Jira, Trello, Agile/Scrum workflows ✅ Third-party integrations: Twilio, Firebase, Google Maps, OpenAI APIs ✅ Testing: Jest, Mocha, PyTest, Selenium Why Work With Me ✅ Proven track record building SaaS platforms from MVP to production scale ✅ Strong balance of AI/ML expertise and software engineering skills ✅ Experience delivering end-to-end mobile and web apps with ML integration ✅ Focused on clean, maintainable, and well-documented code ✅ Committed to scalability, performance, and security :rocket: Let’s bring your idea to life—whether it’s a SaaS product, an AI-powered mobile app, or a full-stack solution built for scale.…

Steven Aleman

I’m a Full-Stack Engineer and Mobile App Developer with strong expertise in AI/ML, SaaS platform development, and scalable cloud solutions. Over the years, I’ve built everything from intuitive mobile applications to enterprise-grade SaaS platforms, combining robust engineering with intelligent automation to deliver impactful results for clients. My strength lies in bridging the gap between machine learning innovation and end-to-end product development. Whether it’s designing multi-tenant SaaS platforms, building full-stack applications, or integrating AI into mobile apps, I ensure the final product is secure, scalable, and user-friendly. Core Skills & Expertise Full-Stack Development ✅ Frontend: React, Next.js, Angular, Vue.js ✅ Backend: Node.js, Express, Django, Flask, FastAPI, Spring Boot ✅ Databases: PostgreSQL, MySQL, MongoDB, Firebase, Redis ✅ API Development: RESTful APIs, GraphQL Mobile App Development ✅ Cross-Platform: Flutter, React Native, Ionic ✅ Native: Swift (iOS), Kotlin/Java (Android) ✅ UI/UX: Material Design, SwiftUI, Responsive UI AI & Machine Learning ✅ Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras ✅ Computer Vision & NLP: OpenCV, Hugging Face Transformers, spaCy ✅ Model Deployment: ONNX, TensorFlow Lite, ML model APIs ✅ Data: Pandas, NumPy, Matplotlib, data preprocessing & pipelines SaaS Platform Development ✅ Multi-tenant architecture, subscription management, and user roles ✅ Payment integrations (Stripe, PayPal, Braintree) ✅ Authentication & security (OAuth2, JWT, SSO) ✅ Analytics dashboards and reporting systems Cloud & DevOps ✅ AWS (EC2, S3, Lambda, RDS, SageMaker), Google Cloud, Azure ✅ CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) ✅ Docker & Kubernetes for containerized deployments ✅ Serverless architecture and microservices Other Tools & Skills ✅ Git/GitHub/GitLab, Jira, Trello, Agile/Scrum workflows ✅ Third-party integrations: Twilio, Firebase, Google Maps, OpenAI APIs ✅ Testing: Jest, Mocha, PyTest, Selenium Why Work With Me ✅ Proven track record building SaaS platforms from MVP to production scale ✅ Strong balance of AI/ML expertise and software engineering skills ✅ Experience delivering end-to-end mobile and web apps with ML integration ✅ Focused on clean, maintainable, and well-documented code ✅ Committed to scalability, performance, and security :rocket: Let’s bring your idea to life—whether it’s a SaaS product, an AI-powered mobile app, or a full-stack solution built for scale.…

Available to hire

I’m a Full-Stack Engineer and Mobile App Developer with strong expertise in AI/ML, SaaS platform development, and scalable cloud solutions. Over the years, I’ve built everything from intuitive mobile applications to enterprise-grade SaaS platforms, combining robust engineering with intelligent automation to deliver impactful results for clients.
My strength lies in bridging the gap between machine learning innovation and end-to-end product development. Whether it’s designing multi-tenant SaaS platforms, building full-stack applications, or integrating AI into mobile apps, I ensure the final product is secure, scalable, and user-friendly.

Core Skills & Expertise
Full-Stack Development
✅ Frontend: React, Next.js, Angular, Vue.js
✅ Backend: Node.js, Express, Django, Flask, FastAPI, Spring Boot
✅ Databases: PostgreSQL, MySQL, MongoDB, Firebase, Redis
✅ API Development: RESTful APIs, GraphQL
Mobile App Development
✅ Cross-Platform: Flutter, React Native, Ionic
✅ Native: Swift (iOS), Kotlin/Java (Android)
✅ UI/UX: Material Design, SwiftUI, Responsive UI
AI & Machine Learning
✅ Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
✅ Computer Vision & NLP: OpenCV, Hugging Face Transformers, spaCy
✅ Model Deployment: ONNX, TensorFlow Lite, ML model APIs
✅ Data: Pandas, NumPy, Matplotlib, data preprocessing & pipelines
SaaS Platform Development
✅ Multi-tenant architecture, subscription management, and user roles
✅ Payment integrations (Stripe, PayPal, Braintree)
✅ Authentication & security (OAuth2, JWT, SSO)
✅ Analytics dashboards and reporting systems
Cloud & DevOps
✅ AWS (EC2, S3, Lambda, RDS, SageMaker), Google Cloud, Azure
✅ CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
✅ Docker & Kubernetes for containerized deployments
✅ Serverless architecture and microservices
Other Tools & Skills
✅ Git/GitHub/GitLab, Jira, Trello, Agile/Scrum workflows
✅ Third-party integrations: Twilio, Firebase, Google Maps, OpenAI APIs
✅ Testing: Jest, Mocha, PyTest, Selenium

Why Work With Me
✅ Proven track record building SaaS platforms from MVP to production scale
✅ Strong balance of AI/ML expertise and software engineering skills
✅ Experience delivering end-to-end mobile and web apps with ML integration
✅ Focused on clean, maintainable, and well-documented code
✅ Committed to scalability, performance, and security

:rocket: Let’s bring your idea to life—whether it’s a SaaS product, an AI-powered mobile app, or a full-stack solution built for scale.

See more

Skills

Experience Level

Android
Expert
AngularJS
Expert
App Design
Expert
Django
Expert
Express
Expert
Flutter
Expert
FlutterFlow
Expert
iOS
Expert
React JS
Expert
React Native
Expert
Vue.js
Expert
Web Design
Expert
Webflow
Expert
See more

Language

English
Fluent

Work Experience

Software Enginer at Cognizant
January 28, 2016 - August 28, 2018
- Led the design and development of scalable software architectures that integrated generative AI models for real-time content creation in high-traffic enterprise applications, ensuring robustness and reliability. - Developed APIs and microservices to facilitate seamless integration of AI models into existing SaaS products, enabling high availability, low latency, and secure data processing. - Designed and implemented CI/CD pipelines using Git, Jenkins, Docker, and Kubernetes for automated deployment, testing, and scaling of generative AI models in production environments. - Optimized model inference performance using GPU acceleration, TensorRT, and edge computing solutions to deliver low latency, high-throughput AI-powered applications. - Built event-driven systems with Apache Kafka to enable real-time personalization of content and automated response generation based on dynamic user behavior and interaction data. - Implemented monitoring, logging, and observability frameworks using Prometheus and Grafana, providing actionable insights into system health, model performance, and operational stability. - Applied advanced NLP and machine learning algorithms for content generation, predictive analytics, and recommendation systems in large-scale enterprise applications. - Conducted comprehensive unit, integration, and system-level testing of generative AI pipelines to ensure high-quality, reliable, and maintainable solutions for mission-critical applications. - Collaborated closely with cross-functional teams, including product managers and UX designers, to align AI model outputs with business goals, improving operational efficiency and user engagement. - Ensured compliance with privacy, security, and ethical AI standards across all generative AI systems, maintaining trust, regulatory adherence, and accountability in production deployments.
Machine Learning Engineer & Backend Engineer at MojoTech
November 29, 2018 - August 30, 2019
- Designed, developed, and deployed deep learning models for generative content synthesis, including CNNs, Transformers, and multimodal architectures, significantly improving text, image, and speech generation for enterprise applications. - Built and optimized recommendation systems using collaborative filtering, content-based approaches, and embeddings to deliver personalized content, increasing user interaction metrics by 20% across multiple platforms. - Applied advanced natural language understanding (NLU) techniques to improve intent classification, query understanding, dialogue generation, and content recommendation pipelines for high-volume production environments. - Collaborated with Google Cloud teams to integrate generative AI models with scalable cloud services, ensuring GPU- accelerated inference, low-latency responses, and high throughput for enterprise clients. - Designed and implemented machine learning pipelines for time-series forecasting, anomaly detection, and trend prediction to improve the personalization and relevance of AI-generated content. - Optimized deep learning workflows and model training procedures using PyTorch, TensorFlow, and Apache Spark for faster iteration cycles, reduced compute costs, and improved overall system performance. - Conducted rigorous evaluation of AI models through cross-validation, hyperparameter tuning, and performance metrics (BLEU, ROUGE, F1-score, perplexity), ensuring high-quality outputs in production settings. - Developed automated retraining and deployment pipelines using MLflow, Docker, and CI/CD frameworks, ensuring continuous improvement and adaptation of generative AI models to evolving datasets. - Implemented AI-driven content moderation and compliance tools to maintain safety, fairness, and regulatory adherence in real-time generative workflows. - Partnered with cross-functional teams to integrate AI solutions into user-facing applications, driving measurable improvements in engagement, personalization, and overall user experience.
AI & Full Stack Engineer at Frame AI
October 28, 2019 - April 28, 2022
- Engineered and deployed predictive and generative machine learning models for IT operations automation, leveraging Python, TensorFlow, and scikit-learn to improve system reliability, reduce manual intervention, and optimize automated root cause analysis workflows. - Developed end-to-end NLP and NLG pipelines for automated content creation, chatbots, and virtual assistant applications, resulting in a 40% reduction in customer response times and significant operational efficiency improvements. - Designed and implemented anomaly detection algorithms using deep learning, time-series forecasting, and statistical analysis to identify unusual patterns in large-scale datasets, enabling real-time operational alerts and enhanced system security. - Architected scalable data engineering pipelines using Apache Spark, Airflow, and SQL, efficiently processing high-volume data for the training and deployment of generative AI models for text, image, and speech synthesis.- Integrated AI models into cloud-based environments, leveraging AWS SageMaker, Azure ML, and containerized microservices for secure, reliable, and production-ready deployment of generative systems. - Built personalized recommendation engines and AI-driven behavioral analytics using embeddings, vector databases, and advanced machine learning techniques, significantly improving user engagement and system optimization. - Conducted extensive feature engineering, hyperparameter optimization, and model fine-tuning to improve generative and predictive AI performance by 25%, ensuring accuracy, robustness, and scalability of deployed models. - Collaborated with engineering and product teams to integrate AI-driven workflows into existing SaaS platforms, enabling automated personalization and dynamic user experiences aligned with business objectives. - Established comprehensive monitoring, observability, and logging frameworks using Prometheus and Grafana, providing actionable insights into AI model performance, operational stability, and system health in production environments. - Led technical workshops and client consultations to implement AI solutions tailored to business requirements, promoting AI adoption and ensuring that deployed models achieved measurable ROI and long-term scalability.
Senior AI & Full Stack Engineer at BlueLabel
July 27, 2022 - April 27, 2025
- Architected, developed, and deployed production-grade generative AI models for text, image, and multimodal content creation, leveraging PyTorch, TensorFlow, and HuggingFace Transformers to deliver outputs with high fidelity, semantic accuracy, and real-world relevance across multiple industry applications. - Led the seamless integration of AI pipelines with scalable cloud infrastructures on AWS, Azure, and GCP, utilizing Docker, Kubernetes, and microservices architectures to ensure high availability, fault tolerance, and optimal resource utilization for LLM-powered solutions. - Applied advanced ensemble learning, hyperparameter tuning, and fine-tuning techniques to enhance generative model performance by 15%, while optimizing evaluation metrics such as BLEU, ROUGE, perplexity, and FID for text and image generation tasks. - Designed and implemented Retrieval-Augmented Generation (RAG) systems with vector search technologies including FAISS, Pinecone, and Milvus, enabling knowledge-intensive applications to retrieve, reason, and generate contextually accurate AI responses in real time. - Conceptualized and executed rigorous A/B testing strategies, leveraging user engagement data, conversion metrics, and recommendation system analytics to continuously refine NLP models and improve overall business impact. - Developed and maintained fully automated ML lifecycle pipelines using MLflow, Git, and CI/CD orchestration frameworks, ensuring continuous model training, evaluation, version control, and seamless deployment across production environments. - Leveraged state-of-the-art NLP techniques, including embeddings, semantic search, and tokenization strategies, to enhance automated content generation, personalized recommendations, and contextually relevant text synthesis for enterprise applications. - Collaborated cross-functionally with product, engineering, and design teams to translate complex business requirements into technical specifications for AI solutions, ensuring alignment with organizational objectives and measurable user experience improvements. - Implemented monitoring, observability, and automated retraining pipelines to proactively maintain model performance, detect drift, and optimize outputs in real-time production settings. - Delivered client-specific AI solutions by building tailored generative AI models that improved personalized content delivery, resulting in a measurable 20% increase in engagement and overall customer satisfaction.

Education

Bechelor's Degree of Computer Science at University of California, Berkeley
September 30, 2011 - September 30, 2015

Qualifications

Add your qualifications or awards here.

Industry Experience

Healthcare, Computers & Electronics, Gaming, Financial Services
    paper End-to-End Software Development & AI/ML Projects

    This portfolio demonstrates my ability to deliver production-ready applications—from full-stack web platforms and SaaS systems to mobile apps enhanced with AI/ML. Built using React, Node.js, Python/Django, Flutter, and TensorFlow, the showcased work reflects my commitment to scalability, performance, and clean design.

    paper AI/ML-Powered Full-Stack & Mobile Development Solutions

    This project demonstrates my expertise in building scalable SaaS platforms, full-stack web applications, and cross-platform mobile apps enhanced with AI/ML capabilities. The website highlights my skills in React, Node.js, Python/Django, Flutter, and cloud services (AWS, GCP, Azure), as well as AI/ML frameworks like TensorFlow and PyTorch. It serves as a central hub to showcase my technical background, previous projects, and the services I offer—ranging from SaaS development and mobile apps to intelligent automation and API integrations.