What are the responsibilities and job description for the Generative AI Engineer position at 360 Technology?
We are seeking a Generative AI Developer to design, develop, and optimize AI-driven applications leveraging state-of-the-art machine learning models. The ideal candidate will have expertise in Large Language Models (LLMs), deep learning, and NLP, along with hands-on experience in deploying AI solutions at scale. You will work closely with data scientists, ML engineers, and software developers to create innovative AI-powered applications.
Key Responsibilities:
AI Model Development:
- Develop, fine-tune, and deploy Generative AI models (e.g., GPT, BERT, Stable Diffusion, DALL·E).
- Implement and optimize large-scale deep learning models for various AI applications.
Data Processing & Engineering:
- Preprocess and curate datasets for AI model training.
- Utilize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient retrieval-augmented generation (RAG).
Application Development & Deployment:
- Integrate Gen AI models into web and mobile applications via APIs.
- Develop AI-driven chatbots, content generation tools, and recommendation systems.
- Deploy AI models using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
Performance Optimization & Research:
- Optimize model efficiency, inference speed, and cost-effectiveness.
- Research and implement cutting-edge advancements in transformer architectures, multimodal AI, and diffusion models.
Collaboration & Documentation:
- Work closely with cross-functional teams, including data scientists, engineers, and product managers.
- Maintain detailed documentation of AI models, APIs, and workflows.
Qualifications:
- Education: Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field.
- Experience:
- 3 years of experience in AI/ML development.
- Hands-on experience with Gen AI models and LLMs.
Technical Skills:
- Proficiency in Python, TensorFlow, PyTorch, Hugging Face Transformers.
- Experience with NLP, computer vision, and reinforcement learning.
- Familiarity with LangChain, OpenAI APIs, and fine-tuning LLMs.
- Knowledge of MLOps, cloud deployment (AWS Sagemaker, Vertex AI, Azure ML).
- Experience with APIs, RESTful services, and microservices architecture.
