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 explicit documentation, present technical results, participate in code reviews, and share knowledge with the team.
Continuously learn by exploring research papers, new tools, and cutting-edge approaches in AI.

Mandatory Technical Skills:
Solid 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

📌 Ai Engineer Kochi
🏢 BEO software
📍 Kochi