Gen AI Developer

Model Development and Deployment:

• Design, fine-tune, and deploy generative AI models (e.g., Llama, GPT, Stable Diffusion) for various applications.

• Train and optimize large language models (LLMs) for tasks such as natural language understanding, summarization, and conversational AI.

1. Data Pipeline Management: * Develop and maintain robust data pipelines for model training and inference.

• Clean, preprocess, and manage large-scale datasets to support AI projects.

1. Integration and Scalability: * Implement ML models in production environments using tools like TensorFlow, PyTorch, or Hugging Face.

• Optimize models for performance, scalability, and cost-efficiency on cloud platforms (AWS, Azure, GCP).

1. Collaboration and Innovation: * Work with product managers, data engineers, and software developers to align AI solutions with business objectives.

• Stay updated with the latest research and advancements in Gen AI and ML.

1. System Monitoring and Maintenance: * Monitor deployed models for performance and accuracy; implement retraining and versioning strategies.

• Ensure systems meet ethical, privacy, and compliance standards

• Experience in machine learning, data science, or related fields.

• Hands-on experience with generative models (e.g., GANs, VAEs, diffusion models) and LLMs.

• Experience with ML frameworks (e.g., TensorFlow, PyTorch, JAX).

• Familiarity with cloud environments (AWS Sagemaker, Azure ML, or GCP AI Platform).

• Strong programming skills in Python (preferred) or similar languages.

• Proficiency in using libraries like Hugging Face, LangChain, or NVIDIA BioNeMo.

• Knowledge of Docker, Kubernetes, and CI/CD pipelines for deployment.

• Understanding of NLP, computer vision, or multimodal AI techniques.

• Strong problem-solving skills and a passion for AI-driven innovation.

• Experience with Retrieval-Augmented Generation (RAG) techniques and vector databases (e.g., Pinecone, Weaviate, Milvus).