Platform Engineer( Python +MLOps)

Job highlights

• Experience with Python, FastAPI, microservices, SQL, MLOps, Docker, and Kubernetes
• Design and maintain scalable backend services, deploy AI/ML models, manage containerized applications, and automate CI/CD pipelines

We are looking for a skilled Platform Engineer with experience in Python, FastAPI, microservices architecture, SQL, AI/ML, and MLOps. The ideal candidate will be responsible for developing scalable backend services, building and deploying AI/ML solutions, and managing containerized applications using Docker and Kubernetes.

Key Responsibilities

• Design, develop, and maintain scalable backend applications using Python and FastAPI.
• Build and manage microservices-based architectures and RESTful APIs.
• Develop and optimize SQL queries, database schemas, and data access layers.
• Implement MLOps practices for machine learning model deployment, monitoring, and lifecycle management.
• Integrate AI/ML models and AI-powered capabilities into production applications.
• Containerize applications using Docker and deploy them using Kubernetes.
• Automate build, testing, deployment, and release processes using CI/CD pipelines.
• Monitor application performance, troubleshoot production issues, and improve system reliability.
• Collaborate with data scientists, AI engineers, DevOps teams, and software developers.
• Follow best practices for security, scalability, performance, and maintainability.

Must-Have Skills

• Strong hands-on experience with Python and FastAPI.
• Good understanding of microservices architecture and REST API development.
• Proficiency in SQL and relational databases.
• Practical experience with MLOps, including model deployment, monitoring, and versioning.
• Understanding of AI/ML concepts and integration of AI models into applications.
• Hands-on experience with Docker for application containerization.
• Experience deploying and managing applications using Kubernetes.
• Knowledge of CI/CD pipelines, Git, and automated deployment practices.
• Strong debugging, problem-solving, and analytical skills.

Good-to-Have Skills

• Experience with cloud platforms such as AWS, Azure, or GCP.
• Familiarity with MLflow, Kubeflow, or similar MLOps tools.
• Knowledge of model serving, model monitoring, and workflow orchestration.
• Experience with message brokers such as Kafka or RabbitMQ.
• Familiarity with logging, monitoring, and observability tools such as Prometheus and Grafana.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.
• Relevant hands-on experience in backend engineering, platform engineering, AI/ML engineering, or MLOps.