Technical Lead of AI Engineering

Join Anaplan as a Technical Lead of AI Engineering, where you will build and lead our Enterprise Grade AI Platform. You will drive the execution of our conversational AI, Generative, and agentic AI roadmap, managing a team of talented engineers while remaining hands-on. This role requires exceptional leadership skills and the ability to solve complex technical problems. You will mentor and grow a team of 5-8 AI engineers, oversee the development of agentic AI systems, and establish engineering best practices for AI development. You will also collaborate with AI Solutions Architects, build and maintain CI/CD pipelines, and communicate progress to senior leadership and cross-functional stakeholders.

• Lead and mentor a team of AI engineers, fostering a culture of technical excellence and innovation.
• Drive end-to-end execution of conversational AI features and oversee the development of agentic AI systems.
• Establish engineering best practices for AI development, including testing strategies, evaluation frameworks, and deployment patterns.
• Strong project management skills with experience using Agile/Scrum methodologies
• Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns
• Experience with agentic frameworks and autonomous agent architectures
• Proficiency in Python and modern software development practices (testing, code review, CI/CD)
• Proven track record of delivering complex technical projects on time with high quality
• Software engineering experience while leading engineering teams
• Excellent communication and stakeholder management abilities
• Demonstrated ability to mentor engineers and build cohesive, productive teams
• Strong hands-on experience building and deploying AI applications in production
• Experience leading teams in fast-growing startups or enterprise software companies
• Knowledge of financial planning, analytics, or business intelligence domains
• Experience with RAG systems, vector databases, and semantic search
• Familiarity with model fine-tuning, evaluation, and continuous improvement workflows
• Background in distributed systems, microservices, or cloud-native architectures