Lead AI Architect, Intapp

• 4+ years of experience in architecting and building AI and ML enterprise-level products or platforms in a SaaS oriented product development organization. Designing cloud native services preferably in Azure
• Demonstrate strong knowledge of AI concepts, technologies, and best practices, and the ability to apply them to solve business problems
• Strong proficiency in Python, SQL, and familiar with frameworks like TensorFlow or PyTorch
• Understanding of machine learning algorithms such as Linear and Logistic regressions, Decision tree, Naive Bayes, KNN, K-means, Random Forest
• Experience in working with and fine-tuning Large Language Models (LLMs) and building scalable applications using LLMs
• 5+ years of experience in architecting distributed B-2-B based systems in a complex business architecture using microservices, event-driven architecture, and distributed computing
• 8+ years of previous software engineering experience with proficiency in:
• One or more modern programming languages like Java, Python or C#
• Database (SQL & NoSQL) and vector database technologies
• Enterprise-level caching, messaging and event streaming solutions (Redis, Kafka, Pub Sub, Spark)
• CI/CD, DevOps, and related tooling, including Azure DevOps, Jenkins, etc

Desirable

• As a Lead AI Architect, you will play a crucial role in designing and developing advanced, scalable, AI/ML solutions for the Intapp products
• You will work with cross-functional teams to understand business requirements and translate them into scalable and efficient AI architectures and solutions
• You will drive architectural decisions and establish data engineering and AI/ML service design standards, policies, and best practices
• Additionally, you will embed Responsible AI principles into every product, focusing on explainability, data drift detection, bias mitigation, and fairness metrics
• You will utilize hands-on experience with evolving AI frameworks and design patterns to ensure successful deployment of AI models in production environments
• You will design end-to-end AI solutions, encompassing data collection, preprocessing, model development, deployment, and monitoring
• Additionally, implementing AI governance principles and methodologies within AI pipelines and solutions will be critical
• Architecting and designing scalable, high-performance, reliable, and cost-effective AI software and services for the Intapp suite of products
• Designing and developing AI architectures, frameworks, and algorithms that can support large-scale and sophisticated AI solutions using Retrieval augmented generation (RAG), Contextual AI, Prompt Engineering etc
• Working on cross-product integration and developing AI solutions that span multiple products
• Working with the Data and AI team leveraging common architectural artifacts to implement data ingestion solutions to support the delivery of AI/ML capabilities deployed across multiple products
• Translating customer requirements into architectural models that operate at large scale, with high performance and deployed on an efficient cloud infrastructure
• Monitoring and measuring the performance, availability, and reliability of AI solutions, and identifying and resolving any issues or risks
• Evaluating and comparing various AI technologies, frameworks, and platforms, such as cloud-based AI services, ML libraries, natural language processing tools, etc
• Defining, deploying and advancing processes for: AI/ML Development and MLOPS including API-first principles, microservices, SDLC/MDLC (model development life cycle), CI/CD, quality, monitoring, observability, security, extensibility and maintainability of software and services
• Leading by example to review code, look for design breaches, provide meaningful and relevant feedback to data scientists and engineers, stay up to date with system changes
• Collaborating with other product architects, data scientists, developers, testers, and business stakeholders to ensure the quality and consistency of AI solutions
• Developing and presenting business cases, roadmaps, and architectures for AI initiatives, and communicating the value and impact of AI solutions to senior management and stakeholders
• Partnering with development leads to create and promote best practices and standards for AI development and deployment