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I have a great passion for solving problems and can help people with various programming languages, databases, information systems, software related projects.
Certified in UiPath, Agentic AI and Microsoft Power Platform, with 15 years of IT experience in analysis, design, development, integration, testing, and maintenance .
Proven expertise in designing and implementing end-to-end Robotic Process Automation (RPA) strategies for web, desktop, mainframe, PDF, SharePoint, and Excel applications using UiPath, integrated with Microsoft and Google OCR technologies.
Delivered 200+ UiPath RPA bots globally, with major projects for US clients, ensuring on-time, quality delivery.
Skilled in Microsoft Power Platform – including Power Automate Flows and Power Automate Desktop.
Hands-on experience in LLM, RAG, Prompt Engineering, Agentic Automation, and AI Agents.
8 years of experience in building and managing AI Systems.
* I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems.
* Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs.
* Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline.
* Provide mentoring to junior developers for ML projects.
Summary:
I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well.
I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well.
I have a cumulative experience of 8 years working in the product and service-based industry for creating Machine Learning projects.
I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on.
Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences.
\- Hands-on experience across several advanced AI and graphics-related domains, including:
* **3D graphics and reinforcement learning pathfinding** for autonomous driving systems, including work related to the **NASA Rover Challenge**.
* **Brain tumor segmentation and classification** using computer vision and deep learning techniques.
* **Video analytics and video classification**, including in-video action recognition and classification workflows.
**📝 Profile Context / Summary**
With 10+ years across enterprise IT and global freelancing, I am an AI-Stack Architect—not just a full-stack developer. In 2026, the industry has shifted decisively: software is no longer just written; it is architected, governed, and continuously learned. The most valuable engineers are no longer those who simply write code, but those who can turn code into cognition—engineers fluent in the AI-stack. I am that engineer.
I design and build agentic platforms—systems where AI doesn't just assist but actively plans, builds, tests, and releases software. I have successfully delivered complex solutions across Medical, Fintech, Employee Management, and SaaS industries, handling everything from AI-native frontends to hyper-scalable microservices with autonomous agent orchestration.
**🤖 AGENTIC AI & MACHINE LEARNING**
2026 is the breakout year of AI inferencing—where trained models generate predictions and outputs from new data. Most AI computing is now spent on inference rather than training. I specialize in:
Agentic AI & Orchestration: LangChain, LlamaIndex, AutoGen, CrewAI—building multi-agent systems that plan, act, and adapt in real time. Full-stack agent platforms combining proprietary training data, planning models, and custom actuation layers.
Generative AI & LLMs: OpenAI GPT-4/API, Google Gemini, Anthropic Claude, HuggingFace Transformers. Retrieval-Augmented Generation (RAG) with vector databases (Pinecone, Chroma, Weaviate, FAISS).
AI-Native Development: 42% of committed code is now AI-generated, projected to reach 65% by 2027. I don't just use AI tools—I orchestrate AI agents across the entire SDLC: planning, design, build, test, deployment, and maintenance.
Deep Learning & Classical ML: TensorFlow, PyTorch, CNNs (Computer Vision), RNNs/LSTMs (Time-Series/NLP), Scikit-learn, Pandas, NumPy.
Model Deployment & Optimization: Model compression, quantization, edge computing, cost-per-prediction optimization.
AI Governance & Security: AI-generated code carries roughly double the security risk violations of human-written code. I implement robust governance, testing, and security controls for AI systems.
**⚛️ MODERN FRONTEND**
React remains the most widely used UI library, with 67% of new enterprise React projects now built on Next.js—a 300% increase since 2023. React Server Components are now the default:
Core: React 19, Next.js 16 (App Router, Server Components, Turbopack), Remix, Svelte 5 (runes), Astro.
Styling: Tailwind CSS, shadcn/ui (1.87M weekly downloads), Framer Motion, Material-UI.
State: Zustand (35% adoption, 35ms updates) vs Redux (38% adoption, 65ms), TanStack Query, Jotai.
Build Tooling: Vite 8 with Rolldown (Rust-based, 10-30x faster builds), Turbopack.
TypeScript: Used by 38% of professional developers, required in 72% of frontend job postings.
**📱 MODERN MOBILE**
React Native's New Architecture (stable since 2024) delivers significant performance improvements through synchronous native module calls:
Frameworks: React Native (Expo), NativeScript, Flutter.
Native: iOS (Swift/SwiftUI), Android (Kotlin & Jetpack Compose).
Emerging: GPU-powered rendering for consumer and enterprise apps.
**⚙️ MODERN BACKEND & API**
Runtimes: Node.js (NestJS, Express), Bun, Deno, Python (FastAPI—57.9% developer usage, up 7% from 2024), Java (Spring Boot 4.0—modularized, Java 25 LTS support).
Spring AI 1.0: Built-in support for integrating LLMs and AI services into enterprise Java applications.
API Design: RESTful, GraphQL (Federation), gRPC, Webhooks, WebSockets.
Event-Driven: Apache Kafka, RabbitMQ, AWS SNS/SQS.
**☁️ DEVOPS & CLOUD (2026 Standard)**
Cloud-native and Serverless adoption has surpassed 70% penetration in enterprise IT. Multi-cloud is now the standard—Gartner projects over 75% of cloud customers will adopt this model:
Cloud: AWS, GCP, Azure (multi-cloud architecture).
Containerization & Orchestration: Docker, Kubernetes (K8s), Helm.
Serverless: AWS Fargate, Lambda, Cloudflare Workers—extending to data pipelines, real-time streaming, and event-driven microservices.
IaC: Terraform, AWS CDK, Pulumi.
CI/CD: GitHub Actions, GitLab CI, ArgoCD, Jenkins.
**🗄️ DATABASES & SEARCH**
Relational: PostgreSQL (PostGIS), MySQL, SQLite.
NoSQL: MongoDB, DynamoDB, Firebase.
Vector (AI): Pinecone, Chroma, Weaviate (for RAG and semantic search).
Search: Elasticsearch, Algolia, Meilisearch.
Caching: Redis, Memcached.
**🔗 CRM, AUTOMATION & INTEGRATION**
In 2026, the CRM market is consolidating around Salesforce, Microsoft, ServiceNow, and HubSpot as a durable challenger. Both platforms have invested heavily in agentic AI, predictive intelligence, and conversational AI:
CRMs: Salesforce (Apex), HubSpot (Agentic Engagement Object), Zoho.
Low-Code/No-Code: Gartner forecasts the low-code market at $44.5 billion in 2026, with 75% of new enterprise apps built on low-code platforms.
Automation: VBA, Excel Macros, Google App Script, Bubble.io, n8n, Make, Zapier.
Citizen Development: 80% of low-code users are now "citizen developers".
**🚀 Why Partner With Me in 2026?**
The software industry has crossed a clear threshold in 2026. Generative AI is no longer just helping developers write code faster—it is reshaping how software is planned, built, tested, and delivered. The role of the developer has evolved from coder to curator of intent, constraints, and outcomes.
**I bring:**
10+ years of battle-tested engineering across the full spectrum.
Agentic AI fluency—the ability to orchestrate AI agents across the entire SDLC.
Systems thinking and architectural judgment—skills that AI cannot replace.
Security-first mindset—AI amplifies what's already there; where code quality is managed, AI accelerates delivery; where it isn't, it accelerates technical debt and security exposure.
Whether you need an AI-native application, a microservices overhaul, an agentic workflow orchestration, or an intelligent CRM integration—I deliver professional, scalable, and governable solutions.
Let's architect intelligence together.
**Contact me today.**
I have good knowledge of predictive analysis, machine learning methodologies, modeling and cluster analysis of large datasets. I am proficient in utilizing SQL in RDBMS concepts. Experience in project documentation (functional and technical), developers and assisting team with project reports and status reports. Highly motivated learner with great team working skills Strong interpersonal, leadership and customer service skills.
I have published paper at BMVC 2017: https://arxiv.org/abs/1710.05158
Deep Learning Blogger: https://medium.com/@tushar20
Technical Skills: Tools: Lucene, Latex, Eclipse
Languages: Java, C, C++, R, Python
Machine Learning Libraries: TensorFlow, Scikit-Learn, Theano, Keras
Relational Database Management Systems: MySQL
Web framework: HTML, CSS, JavaScript
I'm an accomplished polyglot developer with over ten years of extensive experience specializing in design innovation, crafting clean and maintainable code, managing multiple databases and caches, orchestrating CI/CD pipelines, and adeptly resolving even the most challenging production issues through impeccable debugging and analytical skills. Here's more about me https://malaymehta.com
My expertise extends to a diverse range of areas, making me a valuable resource for your software development needs:
- **Application Development:** Whether you're launching a new project from the ground up or seeking guidance on selecting the most suitable technology stack and adhering to best code quality practices, I can provide valuable insights and assistance.
- **Complex Problem Solving:** I excel in crafting intricate coding solutions and swiftly tackling critical bugs that can ablaze your production environment.
- **Clean Coding and Testing:** I'm committed to promoting clean coding practices and achieving comprehensive test coverage to ensure the robustness and maintainability of your software.
- **CI/CD Pipeline Setup:** I deeply understand setting up CI/CD pipelines on various widely recognized platforms, streamlining your development process.
- **Cost-Effective Deployments:** When deploying your application, I prioritize cost efficiency in the cloud environment, helping you transform your ideas into reality without breaking the bank.
- **Security Analysis:** My knowledge includes conducting thorough security analyses to fortify your applications against potential threats.
My dedication to continuous learning has allowed me to accumulate knowledge in many technologies. My diverse experience equips me to assist you on various fronts of software development.
If this resonates with your needs and you recognize that my skills align with your requirements, I encourage you to connect with me. When reaching out, please provide a brief summary of the assistance you're seeking. I stand by the quality of my mentoring sessions, and if you're unsatisfied with the resolution, I'll issue a full refund.
For students with unverified profiles, I offer discounted rates as a gesture of support for your learning journey.
Thank you for considering me as a potential partner in your software development endeavors.
See the power of our Generative AI tutors through glowing user reviews that showcase their successful Generative AI learning journeys. Don't miss out on top-notch Generative AI training.
“Very knowledgeable person, and good communication explain many thing for me. Recommended for AI consultant.“
Dineth Ponnamperuma / Mar 2025
Nitin kalra
Generative AI tutor
“Recommend Silvia! She provided clear and structured explanations to my questions for me, as someone without much previous knowledge in the field, breaking down complex concepts like tokenization, transformers, attention mechanisms, and fine-tuning in an easy-to-understand way.
My worries, about integrating LLM chatbots into my project were solved.
Silvia is patient, knowledgeable, and passionate!“
David Bernhardt / Feb 2025
Silvia Magrelli
Generative AI tutor
“Salako was incredibly helpful and reassuring, answering my many questions and follow-up questions with his expertise. He really helped me weigh each option and weigh the pros and cons of each one. Thank you!“
Aaron A / May 2026
SALAKO TESLIM
Generative AI tutor
“Erick has been great at explaining system architecture for Python and Flask, as well as teaching me JavaScript. He is patient, knowledgeable, and explains everything clearly using a variety of tools and examples. I’ve really appreciated his teaching style and support.“
C / May 2026
Erick Alpizar Rivera
Generative AI tutor
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Arrange regular session times with Generative AI tutors for one-on-one instruction.
Frequently asked questions
How to learn Generative AI?
Learning Generative AIeffectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
Understand the basics: Start with the fundamentals of Generative AI. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of Generative AI, laying a solid foundation for further growth.
Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
Seek expert guidance: Connect with experienced Generative AI tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
Join online communities: Engage with other learners and professionals in Generative AI through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
Build real-world projects: Apply your Generative AI skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Generative AI in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
Stay updated: Since Generative AI is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
How long does it take to learn Generative AI?
The time it takes to learn Generative AI depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
Beginner level: If you are starting from scratch, getting comfortable with the basics of Generative AItypically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of Generative AI’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in Generative AI generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Generative AI, contributing to major projects, and possibly specializing in specific areas within Generative AI.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Generative AI. Engaging with new developments, tools, and methodologies in Generative AI is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
How much does it cost to find a Generative AI tutor on Codementor?
The cost of finding a Generative AItutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
Tutor experience: Tutors with extensive experience or high demand skills in Generative AI typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our Generative AI tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
What are the benefits of learning Generative AI with a dedicated tutor?
Learning Generative AI with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning Generative AI, but exceling in a way that directly aligns with your objectives.
Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning Generative AI can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
Career guidance: Tutors can also offer guidance on how to apply Generative AI in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in Generative AI in a structured, supportive, and effective environment.
How does personalized Generative AI mentoring differ from traditional classroom learning?
Personalized Generative AI mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering Generative AI.