Description

Architect and maintain generative AI applications and their underlying infrastructure, ensuring scalable model deployment and operational oversight.

Responsibilities

• Build GenAI application platforms with reusable frameworks for model building, deployment, and monitoring.
• Automate ML pipelines and optimize models using techniques like LoRA, QLoRA, PEFT, and SFT.
• Implement guardrails, compliance rules, and oversight workflows including approval chains and staged rollouts.
• Develop templates, guides, and sandbox environments to streamline contributor onboarding and experimentation.
• Monitor, debug, and resolve production issues while managing model performance and costs.

Required Skills

• Minimum five years of software development experience with Python and SQL.
• Minimum three years of experience deploying NLP and deep learning models into production within cloud environments.
• Three years of experience using PyTorch, TensorFlow, or MXNet, including GPU cluster optimization.
• Experience building advanced workflows such as retrieval augmented generation (RAG), model chaining, dynamic prompting, and PEFT/SFT using Langchain or similar tools.
• Experience establishing model guardrails.
• Proficiency with Python, SQL, Hadoop, Spark, and Kafka.
• Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline.

Preferred Skills

• Mentor team members on production machine learning code and software engineering best practices.
• Collaborate with data scientists and product managers to deploy features and track algorithmic performance KPIs.

Key Skills

Education

Bachelor's degree