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I will build mlops pipeline and deploy ml llm models on gpu with mlflow
Senior DevOps and MLOps Engineer, CICD, Kubernetes, AWS, Full Stack Engineer
I'm a Senior DevOps & MLOps Engineer with 12+ years helping startups ship secure, scalable, production-ready systems. On the DevOps side, I architect cloud infrastructure across AWS, GCP, Digital Ocea...
Informazioni su questo servizio
Struggling to deploy your AI/ML or LLM models to production? I'm a Senior MLOps and LLMOps Engineer specializing in ML model development & deployment, fine-tuning, end-to-end pipelines, MLflow tracking, LangChain RAG systems and scalable GPU-based LLM deployment.
I build production-grade MLOps pipelines to automate model development, training, fine-tuning, testing, deployment, monitoring and continuous retraining, fast, secure and scalable.
Services I Offer:
- End-to-End MLOps Pipeline Setup (Kubeflow, Airflow)
- AI/ML Model Deployment to Production
- CI/CD Pipelines for ML & LLMs
- Model Monitoring & Drift Detection
- MLflow Experiment Tracking & Model Registry
- Cloud MLOps (SageMaker, Vertex AI, Azure ML)
- GPU Deployment (RunPod, Modal.com, Vast.ai) for LLMs
- LLMOps (GPT, Llama, Ollama, vLLM, Fine-tuning)
- RAG Pipelines (LangChain, Pinecone, FAISS)
- FastAPI Model Serving & REST APIs
- Automated Model Retraining & Versioning
I deploy machine learning, GenAI, RAG systems, fine-tuned LLMs and custom AI applications.
Tech Stack:
Python, MLflow, Kubeflow, FastAPI, LangChain, SageMaker, Vertex AI, RunPod
Ready to deploy your AI/ML model to production? Lets connect!
Linguaggio di programmazione:
Python
•
SQL
•
MLflow
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Amazon SageMaker
Framework:
Scikit-learn
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DeepPy
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Google ML Kit
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PyTorch
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Panda
Il mio portfolio
FAQ
Which ML and LLM models can you deploy?
I deploy all types of ML and LLM models including machine learning, deep learning, NLP, GenAI, RAG systems, GPT, Llama, Mistral and fine-tuned LLMs. Whether it's a classification model, transformer, or open-source LLM, I'll deploy it to production with MLOps best practices for scalability.
Can you fine-tune LLMs and set up RAG pipelines?
Yes, I offer LLM fine-tuning using LoRA and QLoRA on Hugging Face for models like Llama, Mistral and GPT. I also build production-ready RAG pipelines with LangChain and vector databases such as Pinecone, FAISS or Weaviate. Perfect for chatbots, document Q&A, and custom AI assistants.
What MLOps tools and cloud platforms do you work with?
I work with all major MLOps tools including MLflow, Kubeflow, Airflow, FastAPI and BentoML. For cloud deployment, I use AWS SageMaker, Google Vertex AI, Azure Machine Learning and Databricks. For GPU-based LLM deployment, I work with RunPod, Modal and Vast.ai.
Do you provide model monitoring and automated retraining?
Yes, I set up complete model monitoring including performance tracking, drift detection and automated alerts using MLflow. I also build automated retraining pipelines that retrain your models on new data, ensuring your ML system stays accurate in production.
Can you set up GPU-based LLM deployment on RunPod or Modal?
Absolutely! I deploy LLMs on GPU cloud platforms like RunPod, Modal and Vast.ai for cost-effective, scalable inference. I use vLLM, Ollama and Hugging Face Text Generation Inference for high-performance LLM serving with optimized GPU utilization and auto-scaling for production workloads.
How long does it take to build an MLOps pipeline?
Basic ML model deployment takes 3 days, while a complete end-to-end MLOps pipeline with monitoring and retraining takes 5 days. Full MLOps and LLMOps solutions with fine-tuning, RAG and multi-cloud deployment take 7 days. Timelines depend on your project scope, let's discuss for accuracy.
What deliverables will I receive at the end of the project?
You'll receive complete source code, deployed ML/LLM models with API endpoints, MLflow tracking dashboard, monitoring setup and full technical documentation (Premium tier). All code is production-ready, well-commented and follows MLOps best practices so you or your team can easily maintain it.
Do you provide post-delivery support and documentation?
Yes! Standard package includes 3 days post-delivery support, and Premium includes 7 days plus full technical documentation covering pipeline architecture, model deployment steps and maintenance guidelines. I'm here to help troubleshoot, answer questions and ensure your MLOps system runs smoothly.
