Solution Architect AI

About the Role

A MEAN/MERN Solution Architect with AI is a technical professional who designs and oversees the implementation of complex web applications using the MEAN technology stack (MongoDB, ,Nest js, Angular, React and ), acting as a bridge between business requirements and technical solutions by crafting scalable, secure, and efficient architectures tailored to specific project needs, while collaborating closely with development teams throughout the project lifecycle and experience in designing scalable Python-based systems, microservices, and Generative AI solutions, leveraging expertise in AWS, Google Cloud, and AI/ML tools.

Key Responsibilities:

• Requirements Gathering and Analysis: Understand business needs, translate them into technical specifications, and identify appropriate MEAN components to address those needs.
• Architecture Design: Create detailed technical architectures for MEAN applications, including database schema design (MySQL,MongoDB), API endpoints ), frontend structure (Angular), and system integration patterns.
• Technology Selection: Evaluate and recommend suitable libraries, frameworks, and tools within the MEAN stack to optimize performance and scalability.
• Proof-of-Concept Development: Build prototypes to demonstrate the feasibility of proposed solutions and validate technical approaches.
• Development Guidance: Provide technical leadership to development teams, ensuring adherence to architectural guidelines and best practices.
• Performance Optimization: Analyze application performance, identify bottlenecks, and implement optimizations to ensure scalability and responsiveness.
• Security Architecture: Design robust security measures to protect sensitive data, including authentication, authorization, and data encryption.
• Collaboration with Stakeholders: Effectively communicate technical concepts to non-technical stakeholders, gathering feedback and managing expectations.
• Cloud Integration: Design solutions that leverage cloud platforms like AWS or GCP for deployment and scaling.
• Architect and implement scalable cloud-based data warehouses and end-to-end data pipelines using Google Cloud.
• Developed and applied AI/ML models for forecasting (ARIMA), data classification (logistic regression), anomaly detection (autoencoder, LSTM, SVM), and risk identification (Logistic Regression, Random Forest).
• Integrate Generative AI (Vertex AI) for summarization, narrative generation, conversational AI (RAG chatbot), multi-point agentic AI workflows, knowledge mining, reasoning and insight generation, and prompt engineering.
• Implement MLOps practices for model versioning, automated retraining, and performance monitoring.

Required Skills:

• Proficient in MEAN/MERN Stack: Deep understanding of MySQL, MongoDB, Nest js, Angular , ,Python, LangChain and FastAPI including their core concepts and best practices.
• Frontend Development: Expertise in JavaScript, HTML, CSS, and modern frontend frameworks like Angular or React.
• Backend Development: Experience with RESTful API design, data modeling, and asynchronous programming concepts.
• System Architecture: Ability to design scalable, distributed systems considering performance, reliability, and maintainability.
• Cloud Computing: Familiarity with cloud platforms and their services for deployment and management.
• Communication Skills: Excellent written and verbal communication skills to effectively collaborate with cross-functional teams.
• Machine Learning & AI: BigQuery ML (ARIMA, Logistic Regression), Scikit-learn (SVM, Random Forest, Isolation Forest, One-Class SVM), LSTM, DistilBERT, Hugging Face Transformers, Retrieval-Augmented Generation (RAG), Chain-of-Thought Reasoning, Prompt Engineering, Knowledge Mining, Reasoning and Insight Generation (RIG)

Key Differentiators:

• Experience with Microservices Architecture: Understanding how to design and implement microservices using the MEAN stack for modularity and scalability with Gen AI.
• DevOps Practices: Knowledge of CI/CD pipelines, containerization technologies (Docker), and infrastructure automation.
• Security Expertise: Deep understanding of security best practices for web applications, including OWASP guidelines.