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.
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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.
