Generative AI Engineer

Company Description Kendia.AI is an emerging technology company focused on building practical, high-impact solutions powered by generative artificial intelligence. The organization is committed to creating products that make complex processes more intuitive, efficient, and accessible for businesses and end users. Team members collaborate closely across engineering, product, and design to deliver reliable, safe, and scalable AI-driven applications. Kendia.AI values experimentation, continuous learning, and responsible AI practices as core parts of its culture, offering opportunities to work with modern AI tools and infrastructure. The company provides an environment where technical professionals can contribute meaningfully to the design and deployment of next-generation AI systems.

Role Description The Generative AI Engineer will design, build, and optimize AI models and pipelines that power Kendia.AI’s products and internal tools. This full-time, on-site role in the Austin, Texas Metropolitan Area involves experimenting with state-of-the-art generative models, fine-tuning them for specific use cases, and integrating them into production systems. Day-to-day responsibilities include collaborating with product and design teams to translate requirements into technical solutions, preparing and curating datasets, implementing and testing model architectures, and monitoring performance in real-world environments. The engineer will also contribute to evaluation frameworks, safety and alignment mechanisms, and documentation to ensure models are robust, secure, and maintainable. Participation in code reviews, technical discussions, and continuous improvement of development workflows is expected.

Qualifications

Ability to design, implement, and optimize generative AI models (e.g., large language models, diffusion models, transformers) and supporting pipelines.

Strong software engineering skills in languages such as Python, with experience in machine learning frameworks (e.g., PyTorch, TensorFlow) and modern development practices.

Experience with data engineering and preparation, including dataset collection, cleaning, labeling, and feature extraction for training and evaluation.

Familiarity with deploying ML models to production environments, working with APIs, microservices, containers (e.g., Docker), and cloud platforms (e.g., AWS, GCP, Azure).

Understanding of model evaluation, benchmarking, and monitoring, including metrics, experimentation, and A/B testing for generative systems.

Knowledge of responsible AI principles, including bias mitigation, safety, privacy, and alignment considerations for generative models.

Effective collaboration and communication skills, with the ability to work closely with cross-functional teams and document technical designs clearly.

Bachelor’s or advanced degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.

Experience with startup or fast-paced environments and an interest in staying current with advances in generative