AI Engineer

Responsibilities & Key Deliverables

1. Design, develop, and maintain backend services and APIs to support AI/ML applications at scale.
2. Productionize ML and deep learning models, ensuring efficiency, reliability, and low-latency inference.
3. Build scalable microservices and REST/gRPC APIs for deploying AI solutions in enterprise environments.
4. Optimize AI model serving using frameworks such as TensorRT, ONNX Runtime, Triton Inference Server, or FastAPI.
5. Implement MLOps practices including model versioning, monitoring, and automated deployments.
6. Collaborate with AI researchers, data scientists, and product managers to translate prototypes into production-ready systems.
7. Ensure robustness, fault tolerance, and security in backend AI systems.
8. Integrate AI services with enterprise data platforms, cloud systems, and third-party APIs. 
9. Contribute to architecture discussions, design reviews, and performance tuning. 

Experience

2-5 Years of work experience in AI Engineering

Industry Preferred

Experience in working with Applied AI companies

Qualifications

1. Bachelor’s degree / Master’s degree with experience in Computer Science, Software Engineering, Data Science, or related fields. 
2. Preferred to have degree from a Tier-1/2 institute or a globally top-ranked university (as per QS). 

General Requirements

Technical Requirements

1. Strong proficiency in Python and backend frameworks (FastAPI).
2. Expertise in Prompt engineering and working with various LLMs.
3. Experience in productionizing AI/ML models with efficient inference pipelines.
4. Hands-on experience with model deployment frameworks (Triton, TensorRT, TorchServe, ONNX Runtime).
5. Knowledge of cloud platforms (Azure, GCP) and container technologies (Docker).
6. Strong experience with microservices architecture, CI/CD pipelines, and monitoring tools (Prometheus, Grafana).
7. Familiarity with databases (SQL/NoSQL) and scalable data storage solutions.
8. Exposure to LLMs, SLMs, and GenAI model integration into backend systems is a strong plus.
9. Understanding of security, authentication, and performance optimization in large-scale systems.
10. Experience with version control (Git) and Agile development practices 

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