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What We'll Bring:
As a Generative AI Developer, you will play a pivotal role in designing, developing, evaluating and deploying advanced generative AI models. You will be responsible for leveraging RAG (Retrieval-Augmented Generation) and Graph RAG models to create sophisticated AI-driven solutions. This role requires a deep understanding of generative models, machine learning algorithms, and the ability to work on complex AI projects.
Years of Experience: 3-7 Years

What You'll Bring:

Key Responsibilities

• Develop, and optimize generative AI models with a strong focus on RAG and Graph RAG.
• implement end-to-end generative AI solutions, from data preparation to production deployment
• Develop custom AI models and fine-tune pre-trained models for specific client use cases
• Optimize generative models for production, balancing performance, latency
• Design and implement efficient data pipelines for training and serving generative models
• Develop strategies for effective prompt engineering and few-shot learning in production systems
• Implement robust evaluation frameworks for generative AI outputs
• Address challenges related to bias, fairness, and ethical considerations in generative AI applications

• Develop internal tools and frameworks to accelerate generative AI development

• Required
  • Strong programming skills in languages such as Python, Java, or C++
  • Solid understanding of machine learning algorithms and techniques
  • Demonstrated experience with large language models (LLMs) transformers such as GPT, BERT, T5, etc.
  • Practical understanding of generative AI frameworks (e.g., Hugging Face Transformers, OpenAI GPT, DALL-E)
  • Familiarity with prompt engineering and few-shot learning techniques
  • Familiar with MLOps and LLMOps practices, including CI/CD for ML models
  • Strong knowledge of one or more cloud-based AI services (e.g., AWS Bedrock, Azure ML, Google Vertex AI)
  • Deep understanding of state-of-the-art AI architectures (e.g., Transformers, VAEs, GANs, Diffusion Models)
  • Proficiency in Python and software engineering best practices for AI systems
• Preferred
  • Proficiency in optimizing generative models for inference (quantization, pruning, distillation)
  • Experience with distributed training of large-scale AI models
  • Advanced Techniques: Experience with reinforcement learning, unsupervised learning, or graph-based learning models.
  • Agile Methodologies: Experience working in Agile/Scrum environments.
  • Version Control: Proficiency with Git and other version control systems.

• Advanced degree in Computer Science, Machine Learning, or related field with a focus on generative models
• 2+ years of hands-on experience developing and deploying AI models in production environments with 1 year of experience in developing generative AI pilots, proofs of concept, and prototypes