Python Software Engineer — AI/ML Integration & AI Agents

Job Description

Responsibilities

• Integrate ML and neural network models developed by ML Engineers into production applications
• Build and maintain Python-based inference pipelines and services
• Develop software components that connect ML models with existing product functionality
• Develop AI agents and agent-based workflows, including tool calling, external services, and APIs
• Design and implement REST APIs / integration APIs for external products and services
• Develop software for embedded Linux environments
• Optimize inference and application code for devices with limited CPU, memory, storage, or other hardware constraints
• Integrate Python applications with system services, hardware interfaces, databases, message brokers, and external APIs
• Work with ML Engineers to define model input/output contracts and production inference interfaces
• Package and deploy applications and ML components
• Implement logging, monitoring, error handling, and diagnostics
• Write unit and integration tests
• Troubleshoot issues across application, ML inference, operating system, network, and deployment layers
• Participate in architecture and technical design discussions
• Maintain technical documentation for implemented components and integrations

Required Skills

• Strong knowledge of Python and practical experience developing production software
• Good understanding of software engineering principles and application architecture
• Experience building REST APIs and backend services, preferably using frameworks such as FastAPI, Flask, or similar
• Experience working with Linux
• Understanding of processes, networking, filesystems, permissions, services, and Linux command-line tools
• Experience integrating third-party libraries, SDKs, APIs, or external services
• Good understanding of asynchronous programming, multithreading, or multiprocessing concepts
• Experience with Git and standard software development workflows
• Ability to debug complex integration issues
• Understanding of automated testing and code quality practices

ML / AI Integration Skills

Deep ML research experience is not required, but the candidate should be comfortable working with ML models as software components.

Expected knowledge includes:

• Basic understanding of machine learning and neural networks
• Understanding of the typical ML inference lifecycle:
• input → preprocessing → model inference → postprocessing → application logic
• Experience or familiarity with one or more ML frameworks/runtimes such as:
• PyTorch
• TensorFlow
• ONNX / ONNX Runtime
• TensorRT
• OpenVINO
• TFLite
• Ability to work with models, tensors, model inputs/outputs, preprocessing pipelines, and inference APIs
• Ability to collaborate with ML Engineers to move models from experiments into production environments

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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