This is a hands-on engineering role for someone who wants to build real GenAI systems — not prototypes. You'll work across the full lifecycle, from experimentation and prompt/RAG design through production deployment, observability, and iteration, partnering closely with senior engineers, product owners, and technical leadership. • Build features and components for production-grade GenAI products, with a focus on quality, reliability, and performance. • Implement and iterate on LLM-based solutions, including retrieval-augmented generation (RAG), prompt engineering, context Engineering, Loop Engineering and agentic workflows. • Write clean, well-tested, maintainable Python code and contribute to the broader codebase and infrastructure. • Help build evaluation and monitoring pipelines to measure output quality, groundedness, and reliability of GenAI features. • Work with foundation models and GenAI frameworks/tooling, evaluating tradeoffs between approaches under guidance from senior engineers. • Collaborate with Product Owners and cross-functional stakeholders to translate requirements into working technical solutions. • Participate in code reviews, design discussions, and engineering best practices across the GenAI development lifecycle. • Debug and resolve technical issues across the stack, escalating and collaborating with senior team members on complex problems. • (SDE II) Take ownership of medium-complexity features end-to-end, mentor SDE I engineers, and contribute to architecture and design decisions for GenAI components. • 3+ years of industry experience in software engineering, data science, or machine learning. • 1+ years of hands-on experience building GenAI/LLM-based features or applications, ideally in a production setting. • Strong Python skills and experience with modern AI/ML and GenAI frameworks and tooling. • Working understanding of LLMs, embeddings, RAG, prompt engineering, context Engineering, and GenAI architecture patterns. • Experience with cloud-native architectures and deploying scalable applications to production. • Understanding of GenAI evaluation, testing, monitoring, and responsible AI practices. • Ability to work with moderate autonomy, break down ambiguous problems, and communicate technical tradeoffs clearly. • Genuine curiosity and enthusiasm for learning new technologies, tools, and techniques in a fast-moving field. • Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical field. Additional Qualifications • knowledge on fine-tuning or adapting LLMs (e.g., SFT, LoRA/PEFT, instruction tuning), including preparing training datasets or evaluating fine-tuned models against baselines is a plus. • Experience with agentic AI patterns or multi-step LLM workflows is a plus. #LI-MB1 #LI-Hybrid