Senior AI Engineer

We are looking for a capable and agile Senior AI Engineer to join our team and accelerate our AI-based developments. In this role, you will be responsible for the hands-on coding and delivery of AI models and generative AI agentic systems. We need a developer with strong backend foundations who can learn the Intuit AI ecosystem - AI Workbench, quickly and translate business needs into production-ready capabilities. You will embrace a modern, AI-native development workflow vibe, coding using tools like Cursor and Windsurf to rapidly prototype and deliver robust solutions.

Responsibilities:

• Utilise next-generation AI coding assistants (Cursor, Windsurf) to accelerate the software development lifecycle, moving rapidly from ideation to production deployment.
• Design and build AI agents and copilots that utilise Model Context Protocol (MCP) servers and tool-use architectures to automate complex workflows.
• Develop and maintain scalable Retrieval-Augmented Generation (RAG) pipelines and semantic search systems to provide high-quality grounding for AI models.
• Build backend services with AI at their core using Java and Python, ensuring scalability for millions of users and requests daily.
• Implement end-to-end features including prompt engineering, context design, fallback logic, and feature-level configuration.
• Discover, clean, and prepare data sources to refine data pipelines for model training, fine-tuning, and robust evaluation.
• Integrate AI systems with enterprise data sources, APIs, and internal platforms while maintaining strict security and privacy standards.
• Collaborate side-by-side with product managers, data scientists, and backend engineers to define success criteria and align model metrics with business goals.
• Create proofs-of-concept to explore new agent capabilities and frameworks, then harden successful ideas for production use.
• Design for reliability, observability, and cost efficiency in AI-powered systems operating at an enterprise scale.

Requirements:

• 3 - 7 years of industry experience in software engineering.
• 2+ years of specific experience bringing AI models, LLM applications, or machine learning systems to production.
• Bachelor's or Master's in Computer Science, Applied Math, Statistics, or equivalent practical experience.
• Demonstrate expert proficiency in Python and Java, with the ability to manually architect, debug, and optimise complex backend services and distributed systems without reliance on external assistants.
• Exhibit proficiency with AI-assisted coding tools (Cursor, Windsurf) to enhance coding velocity and quality (Note: External requirement).
• Possess deep knowledge of Computer Science Fundamentals, including data structures, algorithms, performance complexity, and memory tuning to write efficient, production-ready code.
• Possess experience building agentic systems, multi-agent orchestration, or systems utilising Model Context Protocol (MCP).
• Maintain deep knowledge of Large Language Models (LLMs), prompt lifecycle management, and fine-tuning techniques such as LoRA and QLoRA.
• Demonstrate implementation experience with Retrieval-Augmented Generation (RAG), vector databases, and semantic search.
• Apply knowledge of data query and data processing tools (i. e., SQL) and familiarity with processing platforms such as Pandas, NumPy, and Spark.

Foundational AI Skills (Basic and Adopting):

• ML Fundamentals: Solid understanding of machine learning principles (training, validation) and statistical modelling techniques (classification, regression).
• Data Handling: Experience with data processing platforms (e. g., Pandas, NumPy, Spark) and SQL.
• Software Engineering Best Practices: Strong grasp of version control (Git), software design patterns, and writing clean, production-ready code.
• Bias for Action: Ability to operate in ambiguity and deliver practical AI solutions that drive measurable business impact.
• Rapid Ecosystem Adoption: Demonstrated ability to pick up new internal tools, cloud technologies (AWS), and AI infrastructure components quickly.
• Production Mindset: Understanding of AI observability, reliability, and cost-efficiency when operating at scale.