AI Engineer ( LLM + Generative AI )

Role & responsibilities

Responsibilities:

• Design, develop, and maintain 

  backend services and APIs

   to support AI/ML applications at scale.
• Productionize ML and deep learning models, ensuring efficiency, reliability, and low-latency inference.
• Build scalable 

  microservices and REST/gRPC APIs

   for deploying AI solutions in enterprise environments.
• Optimize AI model serving using frameworks such as 

  TensorRT, ONNX Runtime, Triton Inference Server, or FastAPI

  .
• Implement MLOps practices including model versioning, monitoring, and automated deployments.
• Collaborate with AI researchers, data scientists, and product managers to translate prototypes into production-ready systems.
• Ensure robustness, fault tolerance, and security in backend AI systems.
• Integrate AI services with enterprise data platforms, cloud systems, and third-party APIs.
• Contribute to architecture discussions, design reviews, and performance tuning.
• Mentor junior engineers and contribute to best practices in AI software engineering.

Educational & Experience requirements

• Bachelors degree with at least 7 years of experience or Masters with at least 4 years of experience in Computer Science, Software Engineering, Data Science, or related fields.
• Preferred to have a degree from a Tier-1/2 institute (IIT/IISc/NITs if studied in India) or a globally top-ranked university (as per QS).

Preferred candidate profile

Technical Requirements

• Strong proficiency in 

  Python

   and backend frameworks (FastAPI).
• Expertise in Prompt engineering and working with various LLMs

  .

• Experience in 

  productionizing AI/ML models

   with efficient inference pipelines.
• Hands-on experience with 

  model deployment frameworks

   (Triton, TensorRT, TorchServe, ONNX Runtime).
• Knowledge of 

  cloud platforms

   (Azure, AWS, GCP) and container technologies (

  Docker

  ).
• Strong experience with 

  microservices architecture, CI/CD pipelines, and monitoring tools

   (Prometheus, Grafana).
• Familiarity with 

  databases

   (SQL/NoSQL) and scalable data storage solutions.
• Exposure to 

  LLMs, SLMs, and GenAI model integration

   into backend systems is a strong plus.
• Understanding of security, authentication, and performance optimization in large-scale systems.
• Experience with version control (Git) and Agile development practices.