AI/ML Architect for Enterprise Solutions

@pkundu25

I am an experienced AI Developer/Architect. Your job caught my eye and looks to be quite interesting to me as I did similar work in recent past. I have good hands-on experience in developing conversational AI chatbot for industrial use cases. I am also architecting solutions AI powered applications using Generative AI/LLMs and building multi-agent systems for automating workflows for enterprise use cases. Why I am the right fit for this position: 1. Expertise in AI/ML/NLP/GenAI and 2. Hands-on experience in building data/RAG pipeline, MLOps and Cloud 3. Design thinking and problem solving mindset 4. Portfolio: https://www.freelancer.in/u/pkundu25?sb=t 5. Notable previous projects - AI Powered Copilot for Text2SQL Query - AI Agent for Risk Mitigation - Semantic search engine 6. Skills and experience (to be shared over chat) Python | Django | Flask | React.js | Agentic AI | Workflow automation | API integration | GPT4 | Gemini/Llama3.2/Mistral | Prompt Engineering | LangChain | MySQL | PostgreSQL | Cloud Computing Let's connect to discuss it further. I am ready to join the work immediately. Thanks for the opportunity.

@kumarashish99

Hi, I’m a hands-on Cloud/DevSecOps and AI infrastructure engineer with 10+ years of experience across AWS, Azure, and GCP, with strong experience in Python, cloud architecture, APIs, containers, CI/CD, security, observability, and production systems. I have been increasingly focused on **AI/ML and Generative AI infrastructure**, including LLM integrations, AI-assisted applications, model/API deployment, data processing, automation, and production operationalization. For enterprise AI solutions, I focus on more than simply connecting an LLM API. I consider **security, data privacy, tenant isolation, retrieval quality, latency, scalability, observability, evaluation, failure handling, and cost** from the architecture stage. I can work across the full lifecycle—from architecture and Python implementation through cloud deployment, CI/CD, monitoring, troubleshooting, and production operations—and can collaborate with engineering/product stakeholders as a hands-on technical lead. I’m comfortable with the required **11:00 AM – 9:00 PM IST** working window and am available for a long-term engagement. I’d be happy to discuss the AI/ML architecture, current stack, and the first priority deliverables. Best,

@TeraByte26

Hi Abhishek, I will design and build a production‑grade AI/ML platform using Python, LLMs, RAG, vector search, and MLOps on Azure, delivering end‑to‑end architecture, APIs, and monitoring. I can deliver a working prototype and deployment plan in four weeks within your 12,500‑37,500 INR budget. Shall I start with a free proof‑of‑concept diagram? Regards, Waiting for your response in chat! Best Regards.

@vishal1145

Your RAG pipeline will fail in production if your vector embeddings drift as your knowledge base scales beyond 10M tokens. Most teams discover this after deployment when retrieval accuracy drops from 85% to 40%. Quick questions - are you planning active monitoring for embedding quality and context relevance? And what's your strategy for handling multi-tenant data isolation in your vector store? Here is the architectural approach: - PYTHON + GENERATIVE AI: Build production RAG system with LangChain/LlamaIndex, implement semantic chunking strategies, and deploy custom evaluation metrics for retrieval precision. - MLOPS/LLMOPS: Design CI/CD pipelines with prompt versioning, A/B testing infrastructure, and real-time observability using Weights & Biases or MLflow for model drift detection. - CLOUD COMPUTING (AWS/AZURE): Architect serverless inference endpoints with auto-scaling, implement vector database sharding across regions, and optimize cost with spot instances for batch processing. I've built similar agentic AI systems for 2 Fortune 500 clients that now process 500K daily queries with 92% accuracy. Let's schedule a 20-minute architecture review to map your specific RAG implementation before you commit infrastructure spend.

@letsUnlockAI

Hi, I understand you’re looking for a hands-on AI/ML Architect to design and deliver scalable enterprise AI, GenAI, LLM/RAG and MLOps solutions from architecture through production deployment. I can work across Python, LLMs, RAG, embeddings, vector search, agentic AI, APIs, cloud, CI/CD, evaluation, monitoring and enterprise security, with a strong focus on reliability, scalability and cost optimization. I can contribute hands-on to architecture, development, troubleshooting and technical leadership, and I’m available for the required IST working hours with a practical production-focused approach.

@KarthikTechCon

Bid Proposal Hi, I'm Karthik, an AI/ML Architect with 15+ years of extensive experience in designing and implementing scalable enterprise solutions. I specialize in AI/ML architecture, Python development, and Generative AI, including LLMs and RAG. I have a proven track record of translating business requirements into secure, production-ready architectures. My hands-on expertise includes model training, deployment, and lifecycle management using MLOps practices. I excel with cloud platforms like AWS/GCP, and I’m adept at integrating AI solutions with enterprise applications and APIs. I am passionate about delivering high-performance, reliable AI solutions while ensuring security, privacy, and cost optimization. With my strong communication skills, I effectively collaborate with stakeholders to drive technical excellence. I am available for the shift from 11 AM to 9 PM IST and can start immediately. Let's discuss how I can contribute to your project’s success. Looking forward to your response! Best, Karthik

@pinkeegupta

Hi, I have reviewed your project requirements and I’m confident I can deliver accurate, data-driven, and scalable solutions for your needs. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju Logical Soft Tech Pvt Ltd, Indore(M.P)

@NEHABHAT92

With over 8+ years of experience specializing in web and mobile app development, languages like Python, PHP, .NET, Java etc have become an integral part of my skillset. Though most of my past projects dealt with Ecommerce and CMS based applications. But I'm a quick learner and passionately committed to expanding my technical abilities in order to meet evolving industry demands. Your project requires an “AI/ML Architect”. I feel that my experience in implementing AI/ML solutions in past project and with cloud platforms like AWS, would allow me to effectively translate business requirements into scalable, secure and production-ready technical architecture. My knowledge of AI tools like LLMs, RAG software's, embeddings etc and MLOps/LLMOps practices tends to align with your description that includes CI/CD implementation ,model versioning ¿

@AhmadXpert2410

Hi there, I can help architect and deliver scalable enterprise AI/ML and Generative AI solutions, with a hands-on focus on Python, LLMs, RAG, embeddings, vector search, agentic AI, and production-grade APIs. I understand the need to translate business requirements into secure, reliable architectures rather than simply building prototypes. My approach would cover the complete lifecycle—from architecture and data pipelines through model/LLM integration, RAG and agent orchestration, deployment, evaluation, monitoring, and LLMOps/MLOps. I can also address enterprise concerns such as scalability, latency, security, observability, CI/CD, model and prompt versioning, and cloud deployment to ensure the solution is production-ready. I bring strong practical experience building AI systems that connect models with enterprise data and applications, and I can contribute at both architectural and implementation levels, including troubleshooting, code reviews, and technical leadership. I’d be glad to discuss your current architecture and help define a robust roadmap for the AI/ML platform. Regards, Ahmad

@tyronet2

You need a hands-on AI/ML Architect who can take enterprise GenAI requirements from architecture through production, with strong Python, RAG, LLM, cloud, and LLMOps experience. I have 9+ years of backend and AI engineering experience with Python, AWS, APIs, real-time systems, and production AI workflows. At Marin Software, I developed AI agents using Python, LangChain, AWS Lambda, and real-time processing pipelines. I’ve also worked with RAG-oriented architectures, embeddings, structured LLM outputs, and API-driven AI integrations. I can design and implement production AI systems covering RAG, vector search, agent orchestration, model evaluation, prompt/version management, CI/CD, observability, security, and scalable deployment. I’m comfortable working hands-on with architecture, coding, troubleshooting, and technical decisions rather than staying at the diagram level. My approach would start by defining the AI architecture and data flow, then building the core services with clear interfaces for models, retrieval, evaluation, monitoring, and deployment. I’d like to understand which cloud platform and LLM/vector stack your enterprise environment currently uses.

@alluankit01

Hi, I can support your AI/ML Architect requirement by designing and delivering enterprise-ready AI, ML, Generative AI, RAG, LLMOps, and cloud-based solutions with hands-on Python development. The best solution is to first understand your business use cases, data sources, enterprise systems, security requirements, cloud environment, model lifecycle needs, and expected AI outcomes. Then I’ll design a scalable architecture covering LLM/RAG pipelines, embeddings, vector search, APIs, model evaluation, monitoring, CI/CD, deployment, and governance. I’m comfortable with Python, Machine Learning, NLP, Generative AI, LLMs, RAG, embeddings, vector databases, prompt/context engineering, agentic AI workflows, APIs, microservices, cloud platforms like AWS/Azure/GCP, containers, MLOps/LLMOps, model monitoring, Responsible AI, and enterprise integration. Deliverables will include: * AI/ML solution architecture * RAG and LLM workflow design * Vector database/search integration * Python-based AI development * Model evaluation and monitoring setup * MLOps/LLMOps pipeline support * API and enterprise system integration * Security and governance planning * CI/CD and deployment guidance * Technical leadership and code review I’ll focus on practical, secure, scalable, and production-ready AI solutions that connect business requirements with reliable implementation and long-term maintainability. Best regards Ankit

@Abror86

Hi-Abror Here From Uzbekistan. "Enterprise AI Architecture Delivery" - I can design scalable enterprise AI systems using Python, LLMs, RAG, cloud, and MLOps practices. I can architect secure AI solutions with LLMs, RAG, embeddings, vector databases, and cloud platforms while integrating enterprise APIs through scalable MLOps workflows for deployment. I will implement model evaluation, monitoring, CI/CD, governance, and documentation ensuring reliable AI operations with production-ready architectures across complex enterprise environments using modern development practices. Can you share your AI architecture goals and preferred cloud platform? Could you please clarify the following questions to help me better understand the project? : 1. What AI solutions are currently planned for implementation? 2. How should these solutions integrate with existing enterprise systems? Looking forward toworking with you.

@GobindSawarn

With over 8 years of experience as a hands-on AI/ML and Generative AI architect, I believe I would be the perfect fit for your project. My deep expertise in Python development, MLOps/LLMOps, and cloud-based ML practices (particularly Azure, AWS, and GCP) make me an ideal candidate. I have also successfully integrated AI solutions into various enterprise applications and data platforms. In addition to my technical skills, my understanding of AI security, responsible AI, and enterprise governance will ensure that your solutions are not just technically sound but also in compliance with industry regulations. My communication and stakeholder management skills further guarantee the smooth translation of business requirements into scalable technical architectures.

@naeem1114

When you pull real-time embeddings from a vector database while the source data schema changes, the retrieval can silently return mismatched results. I’ll add a schema‑version guard in the data pipeline and run a quick sanity check before each query to keep the RAG flow accurate. That way the LLM sees consistent context even as the underlying tables evolve. A common pitfall is hard‑coding API keys in container images, which leaks credentials when images are shared across teams. I’ll keep secrets in the cloud provider’s secret manager and inject them at runtime, so the services stay secure. Ready to start immediately and wire up the full MLOps pipeline for your enterprise use case.

@priyanshuk879

Hi, I can support this role with hands-on AI/ML architecture and production development. I can work across Python, Generative AI, LLMs, RAG, embeddings, and vector databases. I can design scalable AI solutions and connect them with enterprise APIs, data platforms, and cloud services. I also understand the importance of MLOps/LLMOps, CI/CD, monitoring, evaluation, and model lifecycle management. I can help build agentic AI workflows and production-ready microservices with a focus on security and performance. I’m comfortable working with Azure, AWS, or GCP based on the project requirements. I can also support architecture reviews, troubleshooting, code reviews, and technical guidance for the team. I’m available for the required remote shift and can align with your project priorities. Ready to start Immediately. Best Regard, Priyanshu

@indreshpnk

Hello, I understand you need a hands-on AI/ML Architect to design and deliver scalable enterprise AI/ML and Generative AI solutions covering Python, LLMs, RAG, vector search, agentic AI, cloud, and MLOps/LLMOps. The goal is production-ready, secure, scalable, and well-monitored AI architecture. Here’s what I can provide: Design end-to-end AI/ML and GenAI architectures using Python, LLMs, RAG, embeddings, vector databases, and agentic workflows. Build production-ready APIs, model inference, evaluation, monitoring, CI/CD, and MLOps/LLMOps pipelines on Azure, AWS, or GCP. Ensure security, Responsible AI, scalability, performance, observability, and cost optimization across enterprise deployments. I bring over 4+ years of experience in Python, AI/ML, Generative AI, cloud services, APIs, and automation. I focus on practical production implementations, clean architecture, reliable integrations, and strong technical ownership. Just to clarify a few things: Which cloud platform and GenAI use case should be prioritized first? Is this engagement strictly requiring 8+ years of experience, or are strong hands-on skills acceptable? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@InjectAI

I can contribute as a hands-on AI/ML architect across architecture, development, deployment, and optimization of enterprise AI and Generative AI solutions. My core experience includes Python, machine learning, deep learning, computer vision, NLP/LLM applications, RAG, embeddings, LangChain, vector search, APIs, Docker, and production-oriented AI deployment. I have built AI applications involving document processing, RAG pipelines, LLM APIs, computer vision, and edge AI systems. I can help with: • End-to-end AI/ML and GenAI architecture • RAG, embeddings, vector databases and semantic/hybrid search • Agentic AI and LLM orchestration • LLM evaluation, prompt/context engineering and guardrails • Python APIs and microservice architectures • Model training, inference and deployment • MLOps/LLMOps, CI/CD, monitoring and observability • Dockerized production deployments • Enterprise data and AI integration • Performance, scalability, security and cost optimization • Technical documentation, code reviews and mentoring I prefer designing practical architectures that can move from proof of concept to production without unnecessary complexity. Relevant work includes RAG-based applications, AI-powered document processing using vision-language models, real-time computer vision systems on NVIDIA edge hardware, and AI applications integrating external APIs and data pipelines.

@codescraftboy

Having spent numerous years honing my skills in AI/ML engineering and architecture, I am confident that I can bring substantial value to your organization. My experience extends to the full AI development spectrum, with a specific expertise in Python. This proficiency is further complemented by my sound understanding of major cloud platforms such as Azure, AWS and GCP, aligning well with your project requirements. Additionally, I possess a deep understanding of Machine Learning, Deep Learning and NLP, making me uniquely qualified for this role. The integration of AI/ML solutions with enterprise applications, APIs, data platforms and cloud services has become a particular area of strength for me. My exposure ranges from structured and unstructured data management to working with data pipelines and enterprise data platforms. What truly sets me apart is not only my hands-on architectural design capabilities, but also my commitment to responsible AI practices, security, privacy and enterprise governance – aspects that are critical when dealing with sensitive enterprise-scale data. With an holistic approach and an orient towards cost optimization, reliability & high performance, you can trust me to lead this project effectively and efficiently.

@abhilashr53

You need a hands-on AI/ML Architect who can own the full lifecycle — from LLM and RAG architecture through MLOps deployment and enterprise integration — not just a strategist who delegates the hard parts. My plan: architect scalable RAG pipelines with vector databases (Pinecone/Weaviate), implement agentic AI workflows using LangChain/LlamaIndex, enforce MLOps/LLMOps practices with CI/CD, prompt versioning, and observability baked in from day one. Python-first, cloud-agnostic where needed (AWS/Azure/GCP). 8+ years across enterprise AI/ML, production LLM deployments, and Responsible AI frameworks. Comfortable driving architecture decisions solo or alongside engineering teams. Available for the 11 AM–9 PM IST shift immediately, open to C2C, UAN available. Which cloud platform and enterprise stack is the primary target environment?