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

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.

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.