Senior AI Engineer, Intapp

• This role is ideal for experienced engineers who combine strong software fundamentals with applied AI/ML expertise and have hands-on experience building, deploying, and operating AI systems powered by large language models
• Bachelor's or master's degree in computer science, Data Science, or a related field
• 5–10 years of professional experience in software engineering, ML engineering, or data science, with a strong production mindset
• 2+ years of hands-on experience building AI systems using large language models (LLMs)
• Strong proficiency in Python and modern ML frameworks and tooling
• Practical experience with LLMs, NLP systems, and Transformer-based architectures (e.g., GPT, BERT)
• Proven experience designing and deploying RAG systems, agentic workflows, or tool-augmented LLM applications
• Solid understanding of model evaluation, experimentation design
• Experience debugging and improving AI systems through data analysis, prompt iteration, and pipeline optimization
• Experience with vector databases and retrieval systems
• Experience with agent frameworks and orchestration tools (e.g., LangChain)

Desirable

• As a Senior AI Engineer, you will play a critical role in architecting, building, and scaling AI capabilities across our SaaS product portfolio
• You will own the design and delivery of production-grade AI systems—ranging from LLM-powered applications and retrieval-augmented generation (RAG) pipelines to intelligent, agent-based workflows—used in high-stakes enterprise environments
• Architect, design, and build scalable AI systems across the full lifecycle—from experimentation and prototyping to production deployment and monitoring
• Lead the development of LLM-powered features, including prompt engineering, RAG pipelines, and multi-step reasoning workflows
• Design and implement agentic systems for enterprise automation, decision support, and complex workflow orchestration
• Own end-to-end ML pipelines, including data preparation, feature engineering, model selection, fine-tuning, inference optimization, and evaluation
• Define and execute rigorous experimentation frameworks to establish evals, measure accuracy, and cost tradeoffs
• Optimize model performance through data quality improvements, retrieval strategies, hyperparameter tuning, and system-level optimizations
• Partner closely with Product, Data, and Platform teams to translate business requirements into robust AI solutions
• Stay current with AI/ML research and industry trends, and drive the adoption of relevant advances (e.g., Transformers, MoE, distillation, multi-modal models) into production systems
• Provide technical mentorship and guidance to junior engineers, and influence best practices across the AI engineering organization