
Intelligent System Developer
4 days ago
As an AI engineer, you will be responsible for designing and implementing artificial intelligence systems that can learn and adapt to new situations. This involves creating algorithms that can analyze complex data, make predictions, and take decisions based on the analysis.
- Experience with deep learning frameworks such as PyTorch, TensorFlow, and Keras is required.
- Strong understanding of software engineering principles and experience with version control systems like Git is essential.
- Familiarity with containerization using Docker and API design using REST or gRPC is necessary.
Additionally, you should have hands-on experience in fine-tuning large language models (LLMs) and vision language models (VLMs). Experience with OpenCV and its applications in face or posture/form detection is also desirable.
- Experience with vector databases and prompt-engineering strategies is required.
- Proficiency in using the Hub, Transformers, Datasets, and Inference Endpoints is necessary.
- Able to publish, version, and serve models is essential.
The ideal candidate should have a proven track record of delivering sub-100 ms inference latency via quantisation (INT8, FP16), pruning, and hardware-aware neural-architecture-search.
- Experience with orchestrating automated ML pipelines (Kubeflow / MLflow) and model registries is required.
- Ability to expose models through well-documented, version-controlled APIs is necessary.
- Comfort designing SDKs for internal and external consumption is essential.
A clear technical communication, peer mentoring, cross-disciplinary collaboration, and balanced approach to rapid prototyping x production reliability are must-haves for this role.
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