AI Engineer/Architect

Job Description

Role Summary We are looking for a highly motivated AI Engineer with strong experience in Python, FastAPI, and event-driven microservice architecture. You will be instrumental in building intelligent, real-time systems that power scalable AI workflows across our platforms. This role combines deep technical engineering skills with a product-oriented mindset. Key Responsibilities Architect and develop AI microservices using Python and FastAPI within an event-driven ecosystem. Implement and maintain asynchronous communication between services using message brokers like Kafka, RabbitMQ, or NATS. Convert AI/ML models into production-grade, containerized services integrated with streaming and event-processing pipelines. Design and document async REST APIs and event-based endpoints with comprehensive OpenAPI/Swagger documentation. Collaborate with AI researchers, product managers, and DevOps engineers to deploy scalable and secure services. Develop reusable libraries, automation scripts, and shared components for AI/ML pipelines. Maintain high standards for code quality, testability, and observability using unit tests, logging, and monitoring tools. Work within Agile teams to ship features iteratively with a focus on scalability, resilience, and fault tolerance. Required Skills and Experience Proficiency in Python 3.x with a solid understanding of asynchronous programming (async/await). Hands-on experience with FastAPI; knowledge of Flask or Django is a plus. Experience building and integrating event-driven systems using Kafka, RabbitMQ, Redis Streams, or similar technologies. Strong knowledge of event-driven microservices, pub/sub models, and real-time data streaming architectures. Exposure to deploying AI/ML models using PyTorch, TensorFlow, or scikit-learn. Familiarity with containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure). Experience with unit testing frameworks such as PyTest, and observability tools like Prometheus, Grafana, or OpenTelemetry. Understanding of security principles including JWT, OAuth2, and API security best practices. Nice to Have Experience with MLOps pipelines and tools like MLflow, DVC, or Kubeflow. Familiarity with Protobuf, gRPC, and async I/O with WebSockets. Prior work in real-time analytics, recommendation systems, or workflow orchestration (e.g., Prefect, Airflow). Contributions to open-source projects or active GitHub/portfolio. Educational Background Bachelors or Masters degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical discipline. Why Join Transnational AI? Build production-grade AI infrastructure powering real-world applications. Collaborate with domain experts and top engineers across marketplaces, insurance, and Workforce platforms. Flexible, remote-friendly environment with a focus on innovation and ownership. Competitive compensation, bonuses, and continuous learning support. Work on high-impact projects that influence how people discover jobs, get insured, and access personalized digital services.