AI Engineer

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.Role & responsibilities