At Agile Dream Team, we are committed to harnessing the power of Machine Learning and AI to drive innovation. We are looking for a highly skilled MLOps Engineer to help us streamline the deployment, monitoring, and scaling of AI/ML models in production. Learn more about us at: www.agiledreamteam.com Role Overview As an MLOps Engineer, you will be responsible for automating, deploying, and optimizing AI/ML models to ensure efficiency, reliability, and scalability. You will collaborate with Data Scientists, AI Engineers, and DevOps teams to bridge the gap between model development and production deployment. Key Responsibilities - Build and maintain MLOps pipelines for efficient model training, validation, and deployment. - Automate model retraining, monitoring, and scaling using CI/CD and orchestration tools. - Deploy Machine Learning models in cloud, hybrid, and on-prem environments. - Implement model versioning, governance, and explainability for AI solutions. - Optimize ML model performance, inference speed, and resource utilization. - Utilize cloud AI/ML services (AWS Sagemaker, Azure ML, Google Vertex AI). - Work with Docker, Kubernetes, and serverless computing to containerize AI models. - Implement model monitoring and logging (Prometheus, Grafana, MLflow, TensorBoard). - Ensure AI solutions comply with security, scalability, and ethical AI standards. - Collaborate with software engineers, DevOps, and AI teams to enhance AI delivery processes. Required Skills & Experience - Proficiency in MLOps frameworks: MLflow, Kubeflow, TFX, Airflow. - Strong experience with CI/CD pipelines for ML model automation. - Hands-on experience deploying ML models in AWS, Azure, or GCP. - Expertise in Docker, Kubernetes, Terraform, and cloud automation. - Strong programming skills in Python, Bash, and YAML. - Experience with data versioning, model tracking, and pipeline orchestration. - Knowledge of API deployment for AI models using FastAPI, Flask, or GraphQL. - Experience in scaling and optimizing AI inference on cloud and edge environments. - Strong understanding of DevOps principles applied to AI/ML workflows. Preferred Qualifications - Experience in LLM fine-tuning, Retrieval-Augmented Generation (RAG), and AI APIs. - Knowledge of AI model explainability, bias detection, and responsible AI practices. - Experience in distributed computing (Ray, Dask, Spark) for ML workloads. - Contributions to open-source AI/ML projects or publications. - Background in Machine Learning, Data Science, or Cloud Infrastructure. Why Join Us? - Work with a top-tier AI/ML team that’s shaping the future of MLOps. - 100% remote role with a flexible schedule. - Opportunities for growth and continuous learning in AI and MLOps. - Engage in cutting-edge AI projects that create real-world impact. - Competitive salary - Get to know us and apply today! Apply Here Ready to optimize AI at scale? Apply now!