Senior AI Engineer

What You'll Do

Prioritization of High-Impact Automation

• Partner with business teams (e.g., Revenue Ops, Marketing, Finance Ops, Talent Acquisition) to catalog manual, high-frequency workflows and rank them by impact, feasibility, and urgency.

Production Agent Design & Development

• Build multi-step, tool-augmented agent workflows that can plan, execute, observe outcomes, and iteratively improve.
• Design and implement planner–executor and reflection-based architectures to enhance reasoning quality and task reliability.
• Develop stateful agent systems and incorporate human-in-the-loop controls, including approval gates, fallback paths, and escalation mechanisms.

Platform Integration

• Leverage the Agentic AI Foundations team's platform covering agent runtime, orchestration, memory, tool registry, guardrails, and observability.
• Provide feedback that shapes platform priorities.

Innovation & Standards

• Leverage LLMs, multi-agent frameworks, and orchestration platforms to create differentiated internal solutions.
• Stay ahead of emerging AI technologies and regulatory frameworks.

Technical Patterns

• Discover reusable agent design patterns while building real workflows and contribute them back to the Agentic AI Foundations paved paths.

Operational Excellence

• Leverage the evaluation harness and tracing substrate for continuous performance assessment, failure-mode analysis, and optimization of speed and accuracy.

What We're Looking For

Required

• 8+ years of software engineering experience, with at least 2 years focused on AI/ML systems or LLM-powered applications in production.
• Deep hands-on experience with LLM APIs and at least one agentic framework (e.g. LangGraph, CrewAI or AutoGen).
• Strong Python skills and experience building production-grade backend services, APIs, and data pipelines.
• Proven ability to operate in ambiguity: walked into an undefined problem space, figured out what to build, built it, and measured the results.
• Experience shipping AI/automation solutions that directly impacted business operations.
• Strong systems thinking: design for reliability, observability, and maintainability from day one.
• Ability to collaborate directly with non-technical business stakeholders to understand their workflows and translate those into technical solutions.

Preferred

• Experience with multi-agent systems, workflow orchestration, or complex tool-use patterns in production.
• Familiarity with evaluation frameworks (e.g., LangSmith, Weights & Biases, Arize, or custom eval pipelines).
• Experience with RAG pipelines, vector databases, knowledge graphs, or memory/grounding systems.
• Experience with real-time or streaming AI systems.
• Familiarity with AI safety and security practices, including prompt injection prevention, hallucinations mitigation and data protection/privacy.
• Background in a regulated industry (fintech, healthcare, government).
• Experience with agent skill abstraction and structured tool integration via MCP (Model Context Protocol), function calling or similar protocols.
• Experience with AWS-hosted LLM infrastructure (Bedrock, AgentCore/Strands, Lambda, SageMaker).