Generative AI Engineer

Programming:
Expert-level proficiency in Python.

ML Frameworks:
Extensive experience with PyTorch (strongly preferred) and/or Tensor Flow.

LLMs & NLP:
Hands-on experience working with Large Language Models (e.g., via OpenAI API, Hugging Face Transformers, Lang Chain, Llama Index, or custom models). Deep understanding of NLP concepts (tokenization, embeddings, attention mechanisms).

Cloud & MLOps:
Proven experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., Docker, Kubernetes, MLflow, Weights & Biases, TFX).

Problem-Solving:
Strong analytical and problem-solving skills with the ability to iterate quickly from experimentation to production-ready solutions

Design & Development:
Architect, train, fine-tune, and optimize large generative models (e.g., LLMs like GPT, diffusion models like Stable Diffusion, VAEs, GANs) for specific use cases.

End-to-End Pipeline Ownership:
Build robust, scalable data pipelines for pre-processing and curating massive training datasets.

Model Deployment & MLOps:
Implement and manage MLOps practices to deploy models into production, ensuring scalability, low latency, and high reliability. This includes containerization, API development, and continuous integration/continuous deployment (CI/CD).

Performance Optimization:
Apply advanced techniques like Retrieval-Augmented Generation (RAG), fine-tuning, quantization, and distillation to improve model efficiency, accuracy, and cost-effectiveness.

Research &

Innovation: Stay current with the latest academic research and open-source advancements in generative AI. Prototype new ideas and conduct experiments to validate their feasibility and impact.

Collaboration:

Work closely with product managers, data scientists, and software engineers to integrate generative AI capabilities into our products and platforms.