Job Description

ideyaLabs is seeking a talented and innovative Generative AI Engineers with experience in leveraging Low-Code/No-Code (LCNC) platforms. This unique role will be at the forefront of developing and deploying cutting-edge generative AI models while utilizing LCNC tools to accelerate prototyping, integration, and application development. The ideal candidate will possess a strong understanding of generative AI techniques, proficiency in programming, and a passion for exploring the synergy between AI and rapid application development. Responsibilities: Generative AI Model Development & Deployment: Design, develop, train, and evaluate generative AI models for various applications (e.g., text generation, image synthesis, code generation, synthetic data generation). Implement and optimize generative AI models using relevant frameworks (e.g., TensorFlow, PyTorch, Transformers). Deploy and scale generative AI models on cloud platforms (e.g., AWS, Azure, GCP) or on-premise infrastructure. Stay up-to-date with the latest advancements in generative AI research and techniques. Low-Code/No-Code Platform Utilization: Utilize LCNC platforms (e.g., Zoho Creator, Microsoft Power Apps, OutSystems, Mendix, Quixy) to rapidly prototype and build applications that integrate with generative AI models. Develop user interfaces and workflows using LCNC visual development tools. Connect generative AI APIs and services to LCNC applications. Build and deploy data connectors and integrations between various systems using LCNC capabilities. Integration & Application Development: Design and implement robust APIs and integration strategies to connect generative AI models with other systems and applications. Collaborate with software engineers and data scientists to build end-to-end AI-powered solutions. Develop and maintain documentation for AI models, LCNC applications, and integration processes. Experimentation & Innovation: Research and experiment with new generative AI models, LCNC platforms, and integration techniques. Evaluate the feasibility and potential of applying generative AI to solve specific business problems. Contribute to the development of best practices and guidelines for using generative AI and LCNC tools. Collaboration & Communication: Work closely with cross-functional teams, including product managers, designers, and business stakeholders. Communicate technical findings and progress effectively to both technical and non-technical audiences. Participate in code reviews and knowledge-sharing activities. Qualifications: Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Experience: 3 + years of experience in developing and deploying machine learning models, with a focus on generative AI. Proven experience working with at least one or more Low-Code/No-Code development platforms (e.g., Zoho Creator, Microsoft Power Apps, OutSystems, Mendix, Quixy). Experience in building and consuming APIs (RESTful, etc.). Technical Skills: Strong programming skills in Python and experience with relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Transformers). Solid understanding of deep learning concepts and architectures relevant to generative models (e.g., GANs, VAEs, Diffusion Models, Transformers). Experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services. Familiarity with data preprocessing, feature engineering, and model evaluation techniques. Understanding of database concepts and SQL. Proficiency in using visual development tools and integration capabilities of LCNC platforms. Soft Skills: Strong problem-solving and analytical skills. Excellent communication and collaboration abilities. Ability to learn quickly and adapt to new technologies. Proactive and self-motivated with a strong sense of ownership. Passion for innovation and exploring the potential of generative AI. Preferred Qualifications: Experience with specific generative AI applications relevant to our industry (e.g., content generation, synthetic data). Familiarity with containerization technologies (e.g., Docker, Kubernetes). Experience with MLOps practices and tools. Certifications in relevant AI/ML or LCNC platforms.