As a Gen AI Engineer at StatusNeo, you will be responsible for developing, optimizing, and deploying AI models and solutions with a focus on Generative AI, Prompt Engineering, Retrieval-Augmented Generation (RAG) models, and Large Language Models (LLMs).

You will collaborate closely with cross-functional teams to design and implement scalable AI solutions on various cloud platforms.

Responsibilities :

- AI Model Development : Design, train, and fine-tune generative AI models, LLMs, and RAG models to address specific business use cases.

- Prompt Engineering : Develop, test, and optimize prompts to enhance the performance and accuracy of AI models in generating desired outputs.

- Cloud Expertise : Deploy and manage AI models on cloud platforms (e., AWS, Azure, GCP), ensuring scalability, security, and cost-efficiency.

- Data Management : Collaborate with data scientists and engineers to ensure high-quality data ingestion, preprocessing, and feature engineering.

- Model Evaluation and Tuning : Continuously monitor and evaluate model performance, making adjustments to improve accuracy and efficiency.

- Innovation : Stay up-to-date with the latest advancements in AI, LLMs, and cloud technologies, and incorporate new techniques and tools into the development process.

- Collaboration : Work closely with product managers, software engineers, and other stakeholders to integrate AI solutions into products and services.

- Documentation : Maintain comprehensive documentation of models, algorithms, and development processes to ensure transparency and reproducibility.

Requirements :

- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field.

- Proven experience in developing and deploying AI models, particularly in Generative AI, LLMs, and RAG models.

- Hands-on experience with cloud platforms like AWS, Azure, or GCP for AI/ML workloads.

- Strong background in Prompt Engineering and AI model optimization techniques.

Technical Skills :

- Proficiency in programming languages such as Python, TensorFlow, PyTorch, or similar frameworks.

- Experience with NLP, machine learning algorithms, and data preprocessing.

- Knowledge of containerization and orchestration tools (e., Docker, Kubernetes) is a plus.

- Familiarity with MLOps practices and tools for continuous integration and deployment.

Soft Skills :

- Strong problem-solving skills and the ability to work in a fast-paced environment.

- Excellent communication and teamwork abilities.

- Eagerness to learn and adapt to new technologies and methodologies