Staff Engineer, Agentic Workflows

What You’ll Do:

Transform Delivery with Agentic Workflows (Primary Focus)

• Design and implement agentic workflows that materially reduce delivery time and cognitive load
• Build reusable accelerators, templates, and shared services for engineers, product managers, designers, and analysts
• Apply orchestration tools (LangChain, LangGraph, LangSmith, MCPs, n8n) with judgment—knowing where agents add leverage and where simpler solutions win
• Define and evangelize AI-first development practices across teams
• Leverage AI-enabled development environments (Cursor, Windsurf) and establish best practices for human + AI collaboration
• Drive measurable step-changes in team velocity and consistency

Build Production AI Solutions (Secondary Focus)

• Architect and Build LLM- and ML-powered solutions supporting digital journeys, call-center workflows, decisioning, and personalization
• Develop end-to-end systems using Python, JavaScript/TypeScript (optionally Go)
• Partner with Data Science to integrate generative AI with traditional ML (classification, regression)
• Build AI-enabled interfaces using modern web stacks (React, Next.js, TypeScript)
• Ensure scalability, reliability, security, and observability

Engineering & Platform Leadership

• Design modular, reusable AI systems that scale across clients and use cases
• Build and operate production LLM systems, including RAG and agent-based architectures
• Use Databricks for AI development (Spark, Delta Lake, MLflow)
• Apply strong engineering discipline: Git workflows, code reviews, TDD, CI/CD
• Mentor engineers and shape shared standards, patterns, and documentation

What We’re Looking For:

• Senior-level experience building production AI/ML systems
• Proven ability to architect modular, scalable AI solutions
• Strong Python proficiency; solid JavaScript/TypeScript experience
• Production experience with LLM applications (OpenAI, Anthropic APIs)
• Experience building inside of agent frameworks such as LangChain, LangGraph, LangSmith
• Experience with agent orchestration and automation tools (MCPs)
• Experience with Databricks (Spark, Delta Lake, MLflow)
• Understanding of traditional ML and how it complements generative AI
• Experience scaling platforms or practices across teams