- Develop and optimize algorithms and models, with a strong emphasis on Generative AI models (LLMs, Diffusion Models, etc.), fine-tuning, and prompt engineering.

- Write clean, maintainable, and efficient code in Python and other relevant languages.

MLOps & Deployment :

- Implement robust MLOps practices for model versioning, continuous integration/continuous deployment (CI/CD) of ML models, monitoring, and retraining.

- Deploy AI/ML models into production environments, ensuring scalability, reliability, and performance.

- Manage and optimize cloud infrastructure (e.g., AWS, Azure, GCP) for AI/ML workloads.

Research & Innovation :

- Stay abreast of the latest advancements in AI/ML, particularly in Generative AI, large language models (LLMs), and relevant frameworks/libraries.

- Propose and evaluate new AI technologies and methodologies to enhance existing products and explore new opportunities.

- Contribute to the strategic direction of our AI roadmap.

Data & Feature Engineering :

- Collaborate with data scientists and data engineers to collect, clean, preprocess, and transform large datasets for model training.

- Develop effective feature engineering strategies to improve model performance.

Collaboration & Communication :

- Work cross-functionally with product management to understand business needs and translate them into technical specifications.

- Partner with software engineers to integrate AI solutions into existing products and platforms.

Required Qualifications :

- Experience : 5- 7 years of professional experience in an AI Engineer, Machine Learning Engineer, or similar role, with a proven track record of deploying AI/ML solutions to production.

- Programming : Strong proficiency in Python is essential. Experience with other languages like Java, C++, or Go is a plus.

- ML/DL Frameworks : Hands-on experience with major machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow, Keras, Hugging Face Transformers).

Generative AI Expertise :

- Demonstrable experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, RAG (Retrieval Augmented Generation), and understanding of various model architectures (e.g., GPT, BERT, Llama)

- Experience with other generative models (e.g., Stable Diffusion, GANs) is a significant advantage.