AI Engineer

Responsibilities:

• Build and evaluate prototypes/POCs for generative AI features and ideas.
• Fine-tune and adapt open-source LLMs and smaller generative models for specific use cases.
• Collaborate on multimodal experiments involving text, image, and audio data.
• Implement data preprocessing, augmentation, and basic feature engineering for model inputs.
• Design and run experiments, define evaluation metrics, perform ablations, log results, and iterate on models.
• Optimize inference and memory footprint using techniques such as quantization, batching, and basic distillation.
• Contribute to model training pipelines, scripting, and reproducible experiments.
• Work with cross-functional teams (product, infrastructure, MLOps) to prepare prototypes for deployment.
• Write clear documentation, present technical results, participate in code reviews, and share knowledge with the team.
• Continuously learn by exploring research papers, new tools, and innovative approaches in AI.

Mandatory Technical Skills:

• Strong Python programming and familiarity with ML tooling (numpy, pandas, scikit-learn)
• 2+ years hands-on experience with PyTorch and/or TensorFlow for model development and fine-tuning
• Solid grounding in classical ML and deep learning: supervised/unsupervised learning, optimization, CNNs, RNNs/LSTMs, Transformers
• Good understanding of algorithms, data structures, numerical stability, and computational complexity
• Practical experience fine-tuning open models (Hugging Face Transformers, LLaMA family, BLOOM, Mistral, or similar)
• Familiarity with PEFT approaches (LoRA, adapters, QLoRA basics) and efficiency techniques (mixed precision, model quantization)
• Comfortable running experiments, logging results (Weights & Biases, MLflow), and reproducing experiments
• Exposure to at least one cloud ML environment (GCP Vertex AI, AWS SageMaker, Azure AI) for training or deployment
• Effective communication skills for documentation and cross-team collaboration