Senior Software Engineer (AI/ML)
Posted Updated
Responsibilities
Design, build and deploy production-grade machine learning and Generative AI systems. Architect agentic and multi-agent solutions including tool and function calling workflows. Create Retrieval-Augmented Generation (RAG) pipelines that ground outputs in enterprise data. Improve prompt design, safety and response quality through testing and iteration. Define evaluation approaches, metrics and benchmarks for model and system performance. Integrate AI capabilities into existing platforms with data engineers, platform teams and stakeholders. Uphold engineering standards through code reviews, testing, documentation and debugging. Requirements
Python proficiency and hands‑on use of machine learning and deep learning frameworks such as TensorFlow and PyTorch. Experience building with Large Language Models (LLMs) including orchestration frameworks, agents and reasoning patterns. Experience designing and implementing Retrieval-Augmented Generation (RAG) systems for enterprise knowledge and search. Knowledge of data preparation, feature engineering, model selection and production performance considerations. Understanding of core machine learning math including probability, linear algebra and optimization. Familiarity with software engineering practices including Git, testing strategies and maintainable code. Strong problem-solving with the ability to diagnose and resolve complex ML and GenAI system issues.
Design, build and deploy production-grade machine learning and Generative AI systems. Architect agentic and multi-agent solutions including tool and function calling workflows. Create Retrieval-Augmented Generation (RAG) pipelines that ground outputs in enterprise data. Improve prompt design, safety and response quality through testing and iteration. Define evaluation approaches, metrics and benchmarks for model and system performance. Integrate AI capabilities into existing platforms with data engineers, platform teams and stakeholders. Uphold engineering standards through code reviews, testing, documentation and debugging. Requirements
Python proficiency and hands‑on use of machine learning and deep learning frameworks such as TensorFlow and PyTorch. Experience building with Large Language Models (LLMs) including orchestration frameworks, agents and reasoning patterns. Experience designing and implementing Retrieval-Augmented Generation (RAG) systems for enterprise knowledge and search. Knowledge of data preparation, feature engineering, model selection and production performance considerations. Understanding of core machine learning math including probability, linear algebra and optimization. Familiarity with software engineering practices including Git, testing strategies and maintainable code. Strong problem-solving with the ability to diagnose and resolve complex ML and GenAI system issues.