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Software Engineer (Python & MLOps) at IT Labs. We are seeking a versatile . Software Engineer. with strong expertise in . Python backend development. , . cloud engineering. , and exposure to . machine learning workflows. . This role is central to bridging the work between machine learning teams and platform engineering, ensuring smooth integration between models, backend systems, and infrastructure.. You will play a key role in building and maintaining backend solutions using . FastAPI. , developing APIs and SDKs that follow best practices, and enabling seamless data exchange between services and machine learning models. Beyond backend engineering, you will support MLOps processes, explore new tools and libraries, and contribute to internal developer platforms that empower ML engineers to iterate and deploy faster.. The role is highly dynamic — you’ll often work in isolated environments to research, prototype, and implement new concepts that improve workflows, infrastructure, or deployment pipelines.. Responsibilities:. . Design, develop, and maintain backend solutions in . Python. (with . FastAPI. and REST APIs). . . Enable communication between backend systems and machine learning models. . . Act as the technical bridge between ML-focused teams and platform engineering. . . Apply solid OOP and programming principles to deliver clean, maintainable code. . . Implement and maintain unit and end-to-end testing across all services. . . Research and quickly prototype new tools, libraries, and frameworks to improve workflows. . . Support internal developer platforms used by ML engineers to experiment and deploy models efficiently. . . Collaborate with cloud and platform engineers on . AWS-based deployments. , Helm chart updates, and GitHub workflows.. . . Strong background in . software engineering. and backend development. . . Proficiency in . Python. and . FastAPI. . . . Hands-on experience with . REST API. development and best practices. . . Solid understanding of . MLOps workflows. and practical experience integrating ML models into production environments. . . Strong knowledge of . AWS Cloud Services. . . . Experience with testing frameworks and principles (unit, integration, end-to-end). . . Familiarity with . Unix-like operating systems. .. . Nice to Have:. . Experience with . Kubernetes. , . containerization. , and . Kubeflow. . . . Experience with . Helm charts. , . GitHub Actions/Workflows. , and cloud-native CI/CD. . . Knowledge of local Kubernetes environments. (e.g. . Minikube. ). . Company Location: Serbia.