Job Description
We are on the cutting edge of the next industrial revolution. As a Senior Generative AI Engineer, you will not just be writing code; you will be architecting the brain of our AI-driven ecosystem for the year 2026 and beyond. We are looking for a visionary technical leader to define how we leverage Large Language Models (LLMs) and generative adversarial networks (GANs) to transform user experiences.
In this role, you will lead a high-performance team of data scientists and engineers, driving the research and deployment of next-generation AI models that are scalable, efficient, and ethically sound. If you are passionate about the future of Artificial Intelligence and want to leave a lasting impact on the industry, we want to hear from you.
Why Join Us?
- Future-Proof Role: Shape the AI landscape as we prepare for the 2026 technology horizon.
- Competitive Compensation: Industry-leading salary and equity package.
- Remote-First Culture: Work with the best talent from anywhere in the US.
Responsibilities
- Architect and deploy scalable generative AI models (LLMs, diffusion models) using PyTorch and TensorFlow.
- Optimize model inference pipelines to reduce latency and operational costs for real-time applications.
- Lead the research and implementation of Agentic AI workflows to automate complex business processes.
- Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
- Establish best practices for MLOps, model monitoring, and data governance.
- Ensure AI systems are transparent, unbiased, and compliant with emerging ethical guidelines.
- Stay ahead of the curve by evaluating and integrating bleeding-edge research into production systems.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- 5+ years of professional experience in Deep Learning, Natural Language Processing (NLP), or Computer Vision.
- Expert-level proficiency in Python, including frameworks like PyTorch, JAX, or Hugging Face Transformers.
- Strong experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Demonstrated experience deploying LLMs (e.g., GPT-4, Claude, Llama) into production environments.
- Experience with MLOps tools such as MLflow, Airflow, or Vertex AI.
- Strong problem-solving skills and the ability to work in a fast-paced, agile environment.