Lead the architecture, development, and maintenance of generative AI applications and their underlying infrastructure.
Responsibilities
Architect and build GenAI application platforms, including reusable frameworks for model building, deployment, and monitoring.
Automate machine learning pipelines and optimize models using techniques such as LoRA and QLoRA.
Implement guardrails, compliance rules, and oversight workflows like 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.