AI/MLEngineer

## Accountabilities Design, build, and deploy scalable AI and machine learning systems that support predictive analytics, optimization, and generative AI use cases in production environments. Develop reusable Python libraries, data pipelines, and modular components to support ML workflows and accelerate engineering productivity. Contribute to the design and implementation of AI system architectures, including RAG pipelines, LLM integrations, and agent-based workflows. Build evaluation, monitoring, and testing frameworks for ML and AI systems, focusing on performance, reliability, consistency, and fairness. Collaborate with cross-functional teams to translate complex business and technical requirements into actionable ML and software solutions. Optimize data storage, retrieval, and processing through effective database design and query performance improvements. Stay current with advances in machine learning, generative AI, and MLOps, integrating relevant innovations into production systems. Requirements: 7+ years of professional experience in machine learning engineering, software engineering, or data science roles focused on building production-grade ML systems. Advanced degree in Computer Science, Software Engineering, Data Science, or a related field (Master’s preferred, PhD a plus). Strong proficiency in Python and experience building reusable packages, automation tools, and production ML pipelines. Deep understanding of ML system design, including model lifecycle management, MLOps practices, and scalable inference architectures. Hands-on experience with cloud platforms such as AWS, Azure, or GCP, along with containerization and CI/CD workflows. Familiarity with distributed computing frameworks, relational and non-relational databases, and large-scale data processing systems. Experience with generative AI frameworks such as LangChain, LangGraph, Hugging Face, or similar tooling for LLM-based applications. Strong analytical and problem-solving skills with the abil