Generative AI Engineer

Key Responsibilities

- Model Development: Design, implement, and optimize LLMs, GANs, diffusion, RAG models, and VAEs to solve complex problems and create innovative solutions.

- Research and Innovation: Stay abreast of the latest advancements in AI and machine learning, and integrate new techniques and technologies into the company's projects.

- Data Handling: Collect, preprocess, and analyze large datasets to train and validate models, ensuring high accuracy and performance.

- Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate AI models into applications and services.

- Documentation: Maintain comprehensive documentation of models, experiments, and processes to ensure reproducibility and knowledge sharing.

- Performance Monitoring: Monitor and evaluate the performance of AI models in production, and implement improvements as needed.

Qualifications:

- Educational Background: Bachelor's degree (BE/BTech) in Computer Science, Engineering, or a related field, or a Master's degree (MCA) with a strong focus on AI and machine learning.

- Experience: Approximately 5 years of relevant experience in developing and deploying generative AI models.

Technical Skills:

- Proficiency in programming languages such as Python, TensorFlow, and PyTorch.

- Strong understanding of LLMs, GANs, RAG models, and VAEs.

- Experience with cloud platforms (e.g., AWS, Google Cloud) and containerization (e.g., Docker).

- Solid knowledge of data preprocessing, augmentation, and visualization techniques.

Problem-Solving Skills: Ability to tackle complex problems with creative and effective solutions.

Communication Skills: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Teamwork: Strong collaborative spirit and the ability to work effectively in a team-oriented environment.

Adaptability: Capacity to thrive in a fast-paced, ever-changing environment and quickly learn new technologies and methodologies.

Preferred Qualifications:

- Experience with natural language processing (NLP) and computer vision.

- Familiarity with reinforcement learning and other advanced AI techniques