A bit about this role: You'll lead a high-impact team at the core of our data, AI, and intelligence platforms, building the intelligence layer behind internal tools and client-facing AI experiences - from conversation intelligence to data models and LLM agents.
This is a hands-on leadership role requiring strength across people management, architecture, coding, and cross-functional alignment. You bring deep experience in Python, TypeScript, data systems, and production AI workflows.
You'll scale a critical area of the business - coaching engineers, setting technical direction, and partnering across teams to turn ambitious ideas into real systems. Most importantly, you'll lead AI engineering by example, building practical solutions that drive meaningful productivity gains through intelligent automation.
This is not a purely managerial or maintenance role. You'll actively contribute to code and help define how modern AI systems are built and deployed across the organization... shaping a core part of our technical future.
The meaningful work you will tackle: Team Leadership & Execution- Lead, hire, and develop a high-performing Client Intelligence engineering team
- Drive clarity, accountability, and predictable delivery across multiple initiatives
- Foster a culture of ownership, engineering excellence, and continuous improvement
- Coach engineers through hands-on guidance, feedback, and career development
Agentic Systems & AI Innovation- Design and build AI agents and workflows that automate complex processes and improve productivity
- Identify bottlenecks and deploy AI-driven solutions to reduce manual work
- Establish best practices for agent orchestration, tool integration, prompt design, evaluation, and observability
- Promote adoption of modern AI engineering practices across the organization
Architecture & Technical Direction- Own technical direction for client intelligence systems, AI workflows, and supporting data architecture
- Design scalable, maintainable, and observable systems across Python services, data pipelines, and agent-based architectures
- Set and uphold high standards for development, testing, and operational excellence
- Balance speed and rigor to enable rapid, reliable delivery
Hands-On Engineering- Contribute to code in Python, TypeScript, and backend systems
- Build and scale production-grade AI services and workflows
- Review code, improve quality, and troubleshoot complex technical issues
- Rapidly prototype and evolve solutions into stable production systems
Data & Platform Collaboration- Partner with data teams on data lake strategy, modeling, governance, and reliability
- Ensure clean, well-structured data supports AI workflows and product experiences
- Maintain strong architectural discipline around shared data assets
Stakeholder & Product Collaboration- Translate ambiguous business goals into clear engineering plans with cross-functional partners
- Communicate tradeoffs, risks, and priorities effectively
- Align engineering execution with product strategy and business impact
What we're looking for in your background & what makes you a success:- Required Qualifications
- 8+ years building and scaling production software systems
- 2+ years managing and leading engineers
- Strong proficiency in Python, TypeScript, and backend development
- Experience designing distributed systems, APIs, and data-intensive applications
- Hands-on experience building production AI systems, agents, or automation workflows
- Solid understanding of LLMs, APIs, and tool integration patterns
- Experience with data platforms (lakes, warehouses, pipelines) and AWS/cloud-native systems
- Strong problem-solving, debugging, and rapid prototyping skills
- Comfortable contributing in code while leading a team
- Strong communication skills and ability to operate in fast-paced environments
Preferred Qualifications- Experience with multi-agent systems or workflow automation platforms
- Track record of improving engineering productivity through AI or automation
- Experience evaluating AI system performance, reliability, and quality
- Familiarity with modern AI development practices and tools
- Experience with Databricks, DBT, or modern data platforms
- Knowledge of event-driven architectures, orchestration, and observability
- Startup or high-growth environment experience
