Engineering Manager, AI Applications
Lead a team of 4 engineers building AI applications and LLM-powered products at Plaid. Drive strategy for AI integrations, customer support agents, and agentic commerce systems.
About the job
Responsibilities
- Lead a team of 4 engineers (junior to staff level) through goal setting, coaching, and feedback
- Define and drive long-term strategy and manage execution in partnership with technical and product leaders
- Lead projects enabling and scaling business with largest AI customers and partners, starting with personal finance use cases
- Develop and evolve preferred integration patterns for Plaid with AI providers (API adaptations, official Plaid MCP Servers)
- Redefine how Plaid's consumer link experience embeds into conversational interfaces
- Architect the trust layer for agentic commerce
- Scale and extend AI-powered customer experience systems
- Improve homegrown customer support agent with multi-turn and multi-agent systems
- Define scalable offline evaluation for complex multi-turn open-ended tasks
- Research and prototype Human-In-The-Loop Reinforcement Learning (RLHF) for insights flywheel
- Pioneer architecture for customer-specific long-term memory
- Extend agentic systems to support customer journey areas: product recommendation, onboarding, risk diligence, activation, assistance, upselling, cross-selling
Requirements
- 8+ years of industry experience including time as a Staff-level engineer before transitioning to management
- 1+ years of engineering management experience
- Hands-on experience working with LLMs to build and ship products with real user feedback, including:
- Prompt engineering
- Fine-tuning
- Retrieval augmented generation (RAG)
- Semantic search (vector databases, embedding models)
- Agent orchestration frameworks
- Evaluation and monitoring frameworks for open-ended tasks
- Streaming and SSE
- Common UX and design patterns for GenAI-powered products
- Ability to deeply understand customer and user needs through user research and rapid experimentation
- Track record of building and growing high-performing engineering teams
- Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility)
- Extremely curious and passionate about GenAI applications
Nice-to-Haves
- Experience training and/or serving ML models in production, or fine-tuning LLMs for domain-specific use cases
- Comfortable operating in privacy/PII-sensitive environments and applying compliance mitigations
Skills
LLMs, Prompt Engineering, Fine-Tuning, RAG, Vector Databases, Embedding Models, Agent Orchestration, Evaluation Frameworks, Streaming, Sse
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