Senior AI/ML Engineer (Traditional ML + Generative AI)

@neerajdhameliya

Hello, To improve your RAG system, I would use a multi-agent setup with LangGraph and a hybrid search approach (dense + sparse) using Qdrant or Pinecone. This helps reduce hallucinations, improves response accuracy, and lowers latency. It also saves token costs by sending simple structured queries to an XGBoost model, while more complex document-based queries are handled by the LLM agent workflow. I have 8+ years of freelance experience building production-ready AI solutions, combining traditional ML and Generative AI. I develop scalable FastAPI services, deploy with Docker, and manage ML pipelines using MLflow. I'm available to start immediately and would love to discuss your project. Best, Niral

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@corpmember29

Hi, I have 10+ years of experience building production-grade AI/ML solutions, combining classical Machine Learning with Generative AI to deliver scalable, real-world applications. My expertise includes Python, FastAPI, Scikit-learn, PyTorch, LangChain, LangGraph, OpenAI, Claude, RAG pipelines, vector databases (Pinecone/FAISS/Qdrant), PostgreSQL, Docker, Kubernetes, MLflow, and AWS. I've built intelligent chatbots, multi-agent systems, semantic search, AI automation platforms, and predictive ML models, handling the complete lifecycle from data engineering and model development to deployment, monitoring, and optimization. I also have hands-on experience with CI/CD, MLOps, prompt engineering, tool calling, structured outputs, and scalable REST APIs. I write clean, maintainable, production-ready code, collaborate effectively across teams, and enjoy taking ownership of AI projects from research through deployment. I'd be excited to contribute to your team and help build robust, enterprise-grade AI products.

@jarviskb

Hi, I’d be excited to contribute as an AI/ML Engineer with strong experience across classical machine learning, production GenAI systems, RAG, multi-agent workflows, and MLOps. I have hands-on experience building end-to-end ML and AI products using Python, Pandas, NumPy, Scikit-learn, XGBoost/LightGBM/CatBoost, SQL, FastAPI, Docker, cloud services, and modern LLM frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen. I have worked with OpenAI, Claude, Gemini, Llama, Qwen, and other LLMs for production-grade AI applications. On the ML side, I can help with feature engineering, classification/regression models, forecasting, clustering, recommendation logic, model evaluation, experiment tracking, and deployment. On the GenAI side, I have experience building RAG pipelines, vector search, hybrid retrieval, reranking, tool-calling agents, structured outputs, memory systems, prompt optimization, and LLM evaluation frameworks. I am also comfortable owning the production lifecycle: API development, CI/CD, containerization, monitoring, model versioning, performance optimization, and cloud deployment on AWS/GCP/Azure. My focus is not just prototyping, but building reliable, scalable, maintainable AI systems that can run in production. I can work independently, collaborate with product/data/engineering teams, and contribute to architecture decisions, implementation, evaluation, deployment, and continuous improvement.

@PINESCRIPTERR

Hi, I can support this role across both production ML and modern Generative AI systems. My experience covers Python-based ML pipelines, structured-data modelling, feature engineering, evaluation, FastAPI services, RAG workflows, LLM integrations, vector databases, prompt/tool design, and deployment-ready architecture. For classical ML, I’m comfortable with Pandas, NumPy, Scikit-learn, XGBoost/LightGBM-style workflows, SQL-heavy datasets, model validation, experimentation, and monitoring. For GenAI, I can build RAG systems, agent workflows, structured outputs, tool/function calling, memory patterns, retrieval evaluation, reranking, and API integrations with OpenAI/Claude/Gemini or open-source models. My preferred production stack would be Python, FastAPI, PostgreSQL, Redis, Qdrant/Pinecone/Weaviate, LangGraph or LangChain, Docker, GitHub Actions, MLflow, and AWS/GCP/Azure depending on your infrastructure. I focus on reliable architecture: clean services, evaluation loops, logging, cost/latency controls, CI/CD, and clear documentation rather than fragile demos. I can also help with model versioning, experiment tracking, deployment, monitoring, and improving existing AI systems from prototype to production quality. Question 1: Is the first priority classical ML, RAG, multi-agent workflows, or production/MLOps cleanup? Question 2: Are you already using a cloud/vector database stack, or should I recommend one based on the product requirements? Regards, Houssame

@juanpablomacf

I can support you as a Senior AI/ML Engineer, combining classical ML expertise with production‑grade Generative AI systems. I’ve built end‑to‑end pipelines, multi‑agent architectures, RAG systems and scalable MLOps deployments. If helpful, I can explain how I design a RAG pipeline or how I structure a multi‑agent system for reliable orchestration. My approach: • Predictive models with scikit‑learn, XGBoost/LightGBM, feature engineering and evaluation. • LLM apps using LangGraph/LangChain, structured outputs, tool calling and memory. • RAG with Pinecone/Qdrant, hybrid retrieval and reranking. • FastAPI services, Docker/Kubernetes, CI/CD, MLflow, monitoring and versioning. • Automated evaluation pipelines for LLMs, agents and RAG accuracy/latency. • Clean, production‑ready Python code with strong SQL for large datasets. I’ve delivered ML + GenAI systems for enterprise products, owning the full lifecycle from data prep to deployment. Ready to work long‑term and deliver consistently.

@mykhaylob4

✋ Hi there. I can build production-ready traditional ML and Generative AI systems, including multi-agent architectures, RAG pipelines, and scalable ML services. ✔️ I have strong experience across the full AI lifecycle, from predictive modeling and feature engineering to building LLM applications with LangGraph, RAG using vector databases, and deploying APIs with FastAPI and Docker. I will develop this by designing end-to-end ML solutions, building evaluation pipelines, and implementing MLOps practices for monitoring and continuous improvement. Please click the 'Chat' button to start our valuable conversation. Looking forward to collaborating with you! Best regards, Mykhaylo

@gameprogrammer13

This looks like a good fit for my background. I usually write these types of scripts using Python with asyncio. It keeps things stable and easy to schedule. Happy to discuss your specific requirements whenever you're ready.

@datacode0023

Hello, I have over 9 years of experience working on AI projects (generative ai) and have successfully contributed to multiple projects in this field. I also hold a Master's degree in Artificial Intelligence. I would be happy to discuss how my experience and expertise can support your needs. Please feel free to contact me to discuss further. Have a nice day.

@eduard141

Dear Hiring Manager, I am very interested in your AI/ML Engineer position. With strong experience in both traditional Machine Learning and modern Generative AI, I have built and deployed production-ready AI solutions across the entire development lifecycle. My expertise includes: • Python, Pandas, NumPy, Scikit-learn, XGBoost, and SQL for predictive modeling and data analysis. • LLM applications using OpenAI, Claude, Gemini, LangChain, and LangGraph. • Multi-agent systems, RAG pipelines, prompt engineering, tool calling, and memory architectures. • Vector databases including Pinecone, Qdrant, FAISS, and Chroma with hybrid retrieval and reranking. • Production deployment using FastAPI, Docker, Kubernetes, CI/CD, MLflow, and cloud platforms such as AWS and GCP. I have experience developing scalable AI applications, APIs, and intelligent automation systems with a strong focus on performance, maintainability, and real-world business impact. I am comfortable owning projects from research and architecture design through deployment and continuous improvement. I would appreciate the opportunity to discuss your requirements and demonstrate how my experience can contribute to your team and help build innovative AI products. Thank you for your time and consideration. I look forward to speaking with you. Best regards, Eduard

@ashinnd84

Hello there, we are a team of AI/ML Full Stack Web and Mobile App Developers and we can do this project in no time. Thanks Ashish Kumar.

@PrateekP3003

Hi There! In your project description, you are looking for an AI/ML Engineer who can build production-ready machine learning solutions, LLM applications, multi-agent systems, RAG pipelines, and scalable AI services using modern MLOps practices. Right? I AM GLAD TO TELL YOU THAT I HAVE ALREADY WORKED ON SIMILAR AI & MACHINE LEARNING PROJECTS. With my knowledge and skills, I am ready to help you build and deploy production-grade AI systems with the required features and capabilities: End-to-End ML Pipeline Development LLM Applications (OpenAI, Claude, Gemini) Multi-Agent Systems (LangGraph/CrewAI) RAG & Vector Database Integration Prompt Engineering & Tool Calling FastAPI & Scalable REST APIs MLflow, Docker & Kubernetes AWS/GCP/Azure Deployment Model Monitoring & CI/CD Performance Optimization & Documentation I have 5+ years of experience developing AI, ML, and data-driven applications using Python, FastAPI, Scikit-learn, PyTorch, LangChain, LangGraph, PostgreSQL, Redis, Docker, and cloud platforms. I specialize in building scalable, production-ready AI solutions with clean architecture, maintainable code, and reliable deployment pipelines. EVERYTHING AS YOU ARE PLANNING, I WILL EXECUTE. Please get in touch with me via chat so we can continue discussing the project. Thanks & Regards Prateek

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience in software development, with a background in AI platform and machine learning application development. I am familiar with Python, Machine Learning, Data Science, LangChain, LangGraph, AI Agents, Large Language Models (LLMs), RAG, Vector Databases, FastAPI, MLOps, Docker, Kubernetes, TypeScript, and AI Model Development. For this project, I can build production-ready ML and LLM solutions by combining traditional machine learning with scalable RAG pipelines, multi-agent workflows, FastAPI services, and MLOps practices, while optimizing retrieval quality, latency, and model performance for reliable deployment in production environments. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@carlosd178

Hello, As a result of reviewing your project requirements, I understand that you need an AI/ML Engineer who can handle both traditional machine learning and modern GenAI systems from data preparation to production deployment. I have experience handling similar AI and machine learning projects and I’m available to start right now. I bring strong expertise in Python, Machine Learning, Data Science, LLMs, LangChain, LangGraph, AI Agents, RAG, FastAPI, TypeScript, MLOps, Docker, Kubernetes, MLflow, SQL, vector databases, and cloud deployment. One key challenge in projects like this is building AI systems that are not just prototypes, but reliable, scalable, measurable, and maintainable in production. My simple approach would be to first understand the product goal and data flow, then design the ML/LLM architecture, build RAG or agent workflows where needed, add evaluation and monitoring, and deploy through clean APIs with proper versioning and CI/CD. I have a couple of quick questions. • Are you currently building a new AI platform from scratch or improving an existing system? • Which cloud and vector database do you prefer for deployment? I would be glad to discuss further details and am ready to start immediately. Looking forward to hearing from you. Best regards, Carlos.

@KrisuTechkul

With over 5 years of experience building production ML systems, I am well-versed in both traditional Machine Learning and modern Generative AI - making me an ideal fit for your Senior AI/ML Engineer role. My proficiency with classical algorithms, such as supervised and unsupervised learning, clustering, and recommendation systems combined with my skills in Python, Pandas, NumPy, and scikit-learn perfectly aligns with your requirements. I’ve even dabbled in Time Series Forecasting which you mention as a preference. When it comes to Generative AI, I have hands-on experience with Large Language Models (OpenAI etc.), multi-agent architectures, RAG systems, prompt engineering, tool staffing/function calling and much more. Moreover, my familiarity with LangGraph/LangChain frameworks will prove invaluable as you navigate this project. Lastly, having worked on MLOps and Production Engineering for various cloud platforms like AWS and Google Cloud Platform further solidifies my expertise for end-to-end development. Throughout my career, I've excelled at problem-solving through clean code practices and scalable solutions -- resulting in real-world impact for a range of complex business challenges.

@Eliteinno

Hey, the focus on multi-agent systems with explicit planning and orchestration really caught my eye. I've been building these using LangGraph for complex workflows, especially where agents need to dynamically choose tools and manage their own state. The real challenge often comes with robust memory management and ensuring agents don't hallucinate when chaining actions, which I tackle by integrating structured output validation and clear context windows. Are you leaning towards a specific framework like CrewAI or AutoGen for the agent orchestration?

@exequiel12ifran

Hello, I have just read your job description carefully. I have experience building production AI systems with Python, FastAPI, Scikit-learn, LangChain, LangGraph, OpenAI, Claude, vector databases, PostgreSQL, Redis, Docker, and cloud deployments. I have worked on classical ML models, RAG pipelines, multi-agent workflows, prompt optimization, tool calling, evaluation pipelines, and scalable AI APIs. I am comfortable owning projects from data preparation through deployment, monitoring, and improvement. I have one question for you. Are you mainly hiring for a specific AI product already in production, or for building the platform architecture from the ground up? Thanks for considering my proposal, Lautaro

@IbrahimAltaf

I have hands-on experience building LLM-powered chatbots and AI automation systems, working directly with large language models to design conversational flows, prompt logic, and tool integrations for real business use cases. My background is full-stack engineering, which means I bring solid API design, backend architecture, and production deployment skills alongside the AI layer, not just isolated scripts. I understand this role spans classical ML, production RAG systems, MLOps, and multi-agent orchestration at a senior level. My strongest fit is the Generative AI side: LLM integration, chatbot design, and automation workflows, and I would be upfront that my classical ML and MLOps depth is still growing rather than at the 5+ year production level this role describes. If you are open to bringing someone in specifically for the LLM and automation portion of this work, or pairing with your existing ML team, I would be glad to contribute there and grow into the broader scope over time. Happy to share examples of chatbot and automation work I have delivered.