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Mid-level generalist AI role with broad LLM production requirements attracts many applicants.
Requires specialist ML/LLM production experience, making cross-industry transfers limited.
Explicit 5+ years requirement plus mandatory production ML, cloud, and LLM experience enforces strict filters.
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Design, build, optimize, and deploy scalable machine learning and generative AI systems across product ecosystems involving NLP, LLMs, conversational AI, and recommendations.
Convert prototypes and research into production-grade AI services with low latency and high throughput deployed on cloud and container platforms.
Establish evaluation frameworks, monitor AI model performance, and continuously improve reliability through techniques like fine-tuning, prompt engineering, and automated retraining.
5+ years of experience in machine learning, AI engineering, applied research, or data science.
Strong proficiency in Python and modern software engineering practices with experience in PyTorch, TensorFlow, JAX, or Hugging Face Transformers.
Experience building and deploying production-grade AI or machine learning systems on cloud platforms like AWS, GCP, or Azure.
Familiarity with container technologies (Docker, Kubernetes), CI/CD, experiment tracking, and model monitoring tools.
Experienced in end-to-end AI system development including architecture design, deployment, and production monitoring in fast-moving product environments.
Demonstrates ownership of AI technical initiatives and collaborates effectively with cross-functional teams including product, research, and engineering leadership.
Possesses a product-oriented mindset balancing experimentation, engineering quality, and business impact, with clear communication of model capabilities and limitations.