Backend AI Systems Developer

3 weeks ago


Bengaluru, Karnataka, India Aiseberg Full time

Aiseberg is seeking an experienced Backend Engineer to lead the development of our AI systems and databases. This role will play a crucial part in designing, building, and maintaining scalable backend infrastructure that supports our AI applications and handles large-scale data.

Key Responsibilities:
  • System Design & Development: Create high-performance and reliable backend systems and APIs to support AI models and applications.
  • AI Integration: Collaborate with cross-functional teams to integrate AI models into production environments, focusing on seamless model deployment and inference.
  • Database Management: Design, implement, and maintain databases to efficiently handle structured and unstructured data, ensuring optimal performance and data integrity.
  • API Development: Develop and manage RESTful APIs to facilitate interactions between frontend applications, AI services, and backend systems.
  • Collaboration: Work closely with teams, including frontend developers, data scientists, and product managers, to deliver software solutions that meet business needs.
  • Troubleshooting & Support: Identify and resolve issues related to backend systems, APIs, and databases, providing support and maintenance for production systems.

We offer a competitive salary range of $120,000 - $180,000 per year, commensurate with experience, plus benefits and opportunities for professional growth.

Qualifications:
  • Education: Bachelor's degree in Computer Science, Engineering, or a related field from a Tier 1 institution.
  • Experience: 3-4 years of proven experience as a Backend Engineer, with experience integrating AI models and database management preferred.
  • Technical Skills:
  • Proficiency in programming languages such as Python.
  • Strong experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).
  • Knowledge of API development and integration (RESTful).
  • Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) is a plus.

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