MLops Engineer

Position overview

We are seeking an experienced MLOps Engineer with deep expertise in cloud services such as Azure ML and AWS SageMaker, providing end-to-end MLOps operations and services. The successful candidate will cover the entire ML lifecycle, including developing/writing pipelines for model development, preparing training data, training models, deploying models, and monitoring and testing models in production. Additionally, this role requires LLMOps experience, particularly in deploying open-source LLM models such as Llama, Mistral, R1. This role will also involve experimenting with different ML/LLM models, calculating and monitoring evaluation metrics, and providing the best AI solutions for various marketplace-related tasks, such as product category classification, product data enrichment, dynamic price optimization, price and demand forecasting,

Technologies

• MLOps and Cloud Services – Azure ML Studio, AWS SageMaker, MLflow, Databricks
• Containerization and Orchestration – Docker, Kubernetes
• Model Monitoring and Deployment – BentoML, Ollama, Hugging Face
• LLMOps Tools and Libraries – vLLM, LiteLLM, LangChain, Langfuse
• CI/CD and Workflow Automation – Gitlab, Azure DevOps
  • MLOps and ML Lifecycle Management: Develop and optimize MLOps pipelines for scalable model development and deployment.Automate model training, deployment, monitoring, and testing workflows.Manage data pipelines, ensuring efficient training data preparation.Implement model performance tracking and versioning using MLflow
  • LLMOps and Large-Scale AI Deployments: Deploy and fine-tune open-source LLM models for various business use cases. Utilize vLLM, LiteLLM, BentoML, Ollama for optimized LLM inference and deployment.Monitor and evaluate LLM performance across different metrics including latency, accuracy, and cost-effectiveness
  • AI Solutions for Marketplace Optimization:Develop AI-based solutions for product category classification and product data enrichment. Implement dynamic price optimization models and forecast price and demand trends. Build AI systems to select optimal recommerce channels for selling products, based various business factors, including price seasonality and inventory costs.
• MLOps expertise: End-to-end ML pipelines (Azure ML, AWS SageMaker, MLflow), Docker, Kubernetes, CI/CD for ML.
• LLMOps experience: Deploying open-source LLMs (Llama, Mistral, R1), optimizing LLM inference, RAG, fine-tuning.
• Software engineering: Python, ML frameworks (PyTorch, TensorFlow), microservices, data pipelines.
• Problem-solving: Evaluating and fine-tuning ML/LLM models, tracking metrics, optimizing AI solutions.