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 for [mention industry]).
- Familiarity with containerization technologies (e.g., Docker, Kubernetes).
- Experience with MLOps practices and tools.
- Certifications in relevant AI/ML or LCNC platforms.