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**📝 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'm a Highly accomplished and results-oriented Engineer with extensive experience in **Full-stack Development, Artificial Intelligence (AI), Cloud Computing, and MLOps/AI** Pipelines I help engineers here—especially those early in their journey—build real-world skills through code reviews, system design, and project guidance. My approach is grounded in clarity, scalability, and long-term growth.
I love to teach what I know. I think I can explain things easily with examples. I have been working as a Software Engineer for the last 5 years and have worked with some of the world's most reputed companies like **Open AI, Scale AI, Doloras Lab**, etc as a freelancer.
I have a great passion for solving problems and can help people with various programming languages, databases, information systems, software related projects.
Senior Data Scientist with expertise in machine learning, natural language processing, information retrieval, and data engineering. Extensive experience in developing advanced algorithms and solutions across various industries. And, years of tutoring experience!
I started out as a Structural Engineer, graduating from UNIBEN, finishing among the top 5 in my class, before taking a path that led me deep into the world of AI/ML Engineering. That transition wasn't linear, it was built on exposure, experimentation, failure, and a lot of learning along the way. Through it all, one thing has never changed: I'm most alive when I'm at the intersection of technology and real world problems. Growing it, evolving it, building it, scaling it. That obsession has shown up in the work, from building the first-ever AI-powered RFP at TalkDEI, to finishing in the top 5 globally at Hamoye, to consistently scoring 98/100 on technical assessments. I've also had the privilege of mentoring and teaching over 5,000 individuals across various skillsets.
I am a 5th year doctoral student (with a Masters in CS). I've published in top-tier computer science/cybersecurity conferences (including USENIX, ACM CCS, and ACM IMC). My expertise is primarily in Network Security, Network Security, and ML Applications to Security. I’m interested in applying my extensive Cybersecurity and ML knowledge to freelance projects, and am enthusiastic about applying my programming expertise to the problems clients are facing!
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. Website: [roshantanisha.github.io/?ref=codementor](http://roshantanisha.github.io/?ref=codementor) 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.
Data Scientist, Cloud Solution Architect, Machine Learning Algorithm Research Engineer, Kaggle Competition Expert #553 (Top 0.5 %), with extensive experience with developing algorithms, statistical models and deep learning architectures using Python in a Big Data environment. Currently working as a Data Scientist with TimeSeries (IoT) domain, design machine learning experiments and developing efficient sequence models for Time-Series segmentation and classification. I have 8+ yrs. experience with Data Science & AI challenges. • A developer and researcher with expertise in data mining , visualization, modeling and evaluation using Python, Pyspark or R on Cloud Platforms. Intermediate experience with SQL and Java. • Curious and enthusiastic to learn and adapt new ideas, concepts, methods, and technology • Experienced with solving variety of interdisciplinary data problems, designing and managing algorithms/models and data science APIs in cloud based scalable way • Interested in sequence modeling for TimeSeries, NLP, Computer Vision and discrete optimization in biomedical, IT product, operation research, supply chain, or energy domain #DataScientist #MachineLearningEngineer #CloudArchitecture #DataEngineer #SoftwareEngineer #DataScienceConsultant #ResearcherAndAnalyst ✆ Contact at minesh[dot]1291@gmail.com for Consultancy or Positions
I have experience in research, data science, and machine learning. I have worked on projects related to deep learning, renewable energy, and dynamic discount algorithms. My education includes a PhD in Mathematical Modelling and a MicroMasters in Statistics and Data Science.
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