Sr. Backend Engineer (AI / ML Focus)

Company Description

Aecor is a UK-based software company empowering businesses worldwide with innovative, reliable, and scalable digital solutions. We partner with startups, SMEs, and large enterprises to design and develop custom software, enterprise applications, SaaS platforms, and cloud-based systems. Our focus on customer-first service, innovation, and technical excellence sets us apart as a trusted partner for digital transformation. Whether you’re a growing startup or an established enterprise, Aecor delivers software solutions that drive real business impact.

Role Description

We are seeking a Backend Engineer for Vadodara location with strong expertise in Python with Flask or other Python backend frameworks. You will design, develop, and maintain APIs and backend services that

power high-volume, data-intensive applications.

This role involves integrating applications with Azure Functions, building robust APIs for AI-

driven workflows, and ensuring high performance, scalability, and reliability. You will collaborate with frontend, DevOps, and data teams to deliver end-to-end solutions.

Required Skills & Experience

5+ years of experience in backend development.

Proficiency in Python backend frameworks (Flask, FastAPI, or Django).

Strong experience with RESTful API development.

Database expertise : PostgreSQL, MySQL, MongoDB, or Redis.

Experience with message queues (RabbitMQ, Kafka, or Celery).

Solid understanding of software design patterns, microservices architecture, and scalability

best practices.

Good-to-Have Skills

Experience with other backend stacks (Java, Go, C#, Node.js).

Familiarity with React, Angular, or Vue.js to support frontend-backend collaboration when

required.

Basic understanding of CI / CD pipelines, containerisation (Docker), and orchestration

(Kubernetes).

Exposure to Prometheus, Grafana, ELK stack for system monitoring and troubleshooting.

Experience with Azure (preferred) or AWS / GCP, Terraform for infrastructure-as-code, cloud

networking, and security fundamentals.

Understanding of deployment strategies, networking, and scaling workloads in Kubernetes.

Awareness of cloud-native security, API security, and best practices for securing distributed systems.