AI/ML Engineer

Role & responsibilities

1. Fine-tune, optimize and adapt pre-trained machine learning models for specific tasks and datasets.
2. Collaborate with cross-functional teams to understand project requirements and implement AI/ML solutions effectively.
3. Preprocess and clean data for training, ensuring high-quality inputs for optimal model performance.
4. Evaluate model performance using appropriate metrics, troubleshoot issues, and implement necessary improvements.
5. Develop new machine learning models from scratch when required, including model architecture design and training strategies.
6. Stay updated with advancements in AI/ML, particularly in model optimization, deployment, and fine-tuning techniques.
7. Document methodologies, implementations, and results clearly for internal knowledge sharing.
8. Provide technical support and guidance for voice and video processing projects, collaborating with dedicated specialists when required.
9. Work on Python-based projects, ensuring scalable and efficient implementations.

Preferred candidate profile :

1. Bachelor's or Masters degree in Computer Science, Data Science, Machine Learning, or a related field.

2. Strong analytical and troubleshooting skills, with the ability to work in a fast-paced, collaborative environment.

3. Demonstrates the ability to quickly learn, adapt, and grasp new concepts, technologies, and processes efficiently.

4. Strong Python proficiency with experience in ML frameworks like TensorFlow and PyTorch, and libraries such as NumPy and Pandas.

5. Expertise in model fine-tuning, optimization, and compression, including techniques like quantization, pruning, distillation, and knowledge transfer.

6. Fundamental understanding of AI architectures, including Transformers, CNNs, RNNs, and GANs, with familiarity in NLP techniques and language model optimization.

7. Experience deploying models locally, on edge devices, and knowledge of cloud deployment (AWS). ONNX, TensorFlow Serving, or similar tools are a plus.

8. Strong grasp of model evaluation and tuning, including cross-validation and hyperparameter optimization.

9. Familiarity with Hugging Face and LangChain.

10. Desirable but not mandatory: experience in voice/video data processing and multi-agent AI architectures.