Senior Backend Engineer (Python/Node.js)

About the Role
the company is seeking a Senior Backend Engineer to architect and build scalable APIs and data processing pipelines powering AI-driven systems. The role involves integrating ML models into production workflows, managing data orchestration, and ensuring the overall reliability, security, and performance of AI services.

Key Responsibilities

• Design and develop scalable backend services using Python, Django, Flask, Node.js, and Nest.js.
• Build and maintain scalable REST and GraphQL APIs.
• Integrate machine learning inference endpoints, batch jobs, and model serving workflows.
• Collaborate with DevOps and ML engineers to deploy models, manage feature stores, and handle large datasets.
• Implement secure, high-performance backend systems with caching, queuing, and monitoring.
• Partner with AI engineers to wrap ML models into APIs and deploy them into production environments.
• Develop reusable, maintainable modules and implement unit and integration testing.
• Support CI/CD pipelines, containerized deployments, and cloud-based backend infrastructure.

Required Skills & Qualifications

• 5+ years of professional backend development experience.
• Strong hands-on experience with Python, Django, and Flask — mandatory.
• Strong experience with Node.js and Nest.js.
• Proficiency in TypeScript, REST/GraphQL APIs, PostgreSQL, and MongoDB.
• Experience designing and implementing scalable and event-driven architectures using Kafka and/or RabbitMQ.
• Hands-on experience with at least one major cloud platform: AWS or Azure.
• Strong experience with Docker and Kubernetes.
• Experience with API development, backend security, performance optimization, testing, and CI/CD.
• Ability to work closely with AI/ML and DevOps teams to productionize AI solutions.

Optional but Desirable (AI-Focused Skills)

• Experience integrating ML model serving frameworks such as FastAPI, Triton, or TorchServe.
• Exposure to data pipeline and orchestration tools such as Airflow, Kubeflow, or Dagster.
• Understanding of AI/ML observability tools such as Prometheus, EvidentlyAI, or Seldon Core.
• Experience working with ML inference, model APIs, feature stores, or large-scale data processing.