
About the Role
As an AI Generative Engineer at Acorn Research, you will be responsible for developing, optimizing, and deploying state-of-the-art generative models. Your work will span from research and prototyping to production-level implementation, contributing to projects that redefine how data and creativity converge. This role demands a blend of deep technical expertise, creative problem-solving skills, and a passion for pioneering AI solutions.
Key Responsibilities:
Model Development & Innovation:
Design, develop, and optimize generative AI models (e.g., GANs, VAEs, transformer-based architectures) for a variety of applications.
Experiment with novel architectures and techniques to improve model performance and robustness.
Collaborate with research teams to translate cutting-edge academic insights into practical, deployable solutions.
Data Management & Preprocessing:
Work closely with data engineers and domain experts to curate and preprocess large, complex datasets.
Implement data augmentation and cleaning techniques to ensure high-quality inputs for model training.
Develop pipelines that streamline data flow and integration with AI models.
Production & Deployment:
Collaborate with software engineering teams to integrate AI models into production environments.
Develop APIs, microservices, and deployment strategies to ensure scalable and reliable model performance.
Monitor and optimize deployed models to maintain performance and accuracy over time.
Research & Collaboration:
Stay abreast of the latest research in generative AI, machine learning, and related fields.
Contribute to white papers, technical blogs, and presentations to showcase innovative solutions.
Collaborate cross-functionally with teams in product development, data science, and business strategy to align AI initiatives with organizational goals.
Technical Leadership:
Provide technical mentorship and guidance to junior engineers and data scientists.
Lead code reviews, maintain coding standards, and ensure best practices in AI model development.
Participate in brainstorming sessions and contribute ideas for new projects and improvements to existing processes.
Education:
Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related field is required; Master’s or Ph.D. is preferred.
Experience:
3-5 years of professional experience in AI/ML engineering, with a focus on generative models.
Demonstrable experience with deep learning frameworks such as TensorFlow, PyTorch, or similar.
Proven track record of deploying AI solutions in production environments.
Technical Skills:
Strong proficiency in Python and experience with AI libraries (e.g., Hugging Face Transformers, Keras).
In-depth understanding of machine learning algorithms, neural network architectures, and optimization techniques.
Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization (e.g., Docker, Kubernetes).
Soft Skills:
Excellent problem-solving abilities and a creative approach to tackling complex challenges.
Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders.
Demonstrated ability to work both independently and collaboratively in a fast-paced, innovative environment.
Certifications:
Relevant certifications in AI/ML or data science are advantageous but not mandatory.