Senior AI Product Engineer - Growth and Transformation job at Red Ventures in Charlotte, NC

Title: Senior AI Product Engineer | Growth and Transformation

Location: Charlotte United States

Job Description:

This role is not open to visa sponsorship or transfer of visa sponsorship including those on H1-B, F-1, OPT, STEM-OPT, or TN visa, nor is it available to work corp-to-corp.

This role requires a hybrid schedule and will be based in our South Charlotte, NC Headquarters (Tuesday through Thursday) and work fully remotely on Mondays and Fridays each week.

We're hiring a Senior Engineer who builds with AI by default. Someone for whom agentic tools are the IDE, not the side project. You'll own the technical direction of our personalization and data platform, built on Go microservices, TypeScript applications, and Python-based AI agents. Your job is to ship product features, make the platform smarter and faster, and raise the bar for how the team builds software. You're a senior IC with tech-lead scope: you own technical direction for your domain, influence architecture decisions across the team, and lead by building, not by org chart.

What You'll Do

Ship Product & Platform Features

Architect and evolve the personalization and data platform powering our consumer experiences

Design high-performance backend services in Go and full-stack features in React + TypeScript, built for scalability, observability, and maintainability

Build RAG pipelines, semantic search, and LLM-powered product features, including embedding models, vector stores, reranking, and eval-driven quality loops

Drive CI/CD and infrastructure maturity via GitHub Actions, Terraform, and AWS

Build AI Into the Platform

Design and ship multi-agent systems using LangGraph, AutoGen, or custom orchestration that operate across the development lifecycle

Break down our multi-language codebase into AI-addressable, agent-ready modules: clean interfaces, well-scoped context, documented patterns

Own the agentic development scaffolding: tool integrations, MCP servers, and shared infrastructure that make every engineer on the team more effective

Multiply the Team's Output

Use agentic coding tools (Claude Code, Codex, Cursor, or whatever's next) as co-engineers, not autocomplete, and help the team do the same

Build shared configurations, custom tool integrations, and workflow hooks that encode team conventions and eliminate repeated decisions

Spot opportunities to automate engineering toil (test generation, PR summarization, migration scripts, dependency upgrades, documentation) and build the tooling to make it happen

Track and share measurable productivity gains with engineering leadership

Raise the Engineering Bar

Run pairing sessions, internal demos, and workshops that build real AI capability across the team

Define practical AI engineering standards: prompt engineering practices, context window management, human-in-the-loop thresholds, and eval frameworks

Mentor mid-level and junior engineers on how to build effectively with AI as a core skill

Partner with Engineering Leadership to shape the AI adoption roadmap across the portfolio

What You Bring

Must-Have

An established AI-native development practice: agentic tools are part of your daily workflow, with results to show for it. Faster delivery, fewer manual steps, higher output

6+ years of software engineering experience with a track record of technical leadership on complex systems

Expert in TypeScript (frontend and backend) and production-grade Go for high-performance services

Strong React and modern frontend architecture experience

Hands-on experience with LLM APIs (Anthropic Claude preferred): prompt engineering, tool use, structured outputs, streaming, context management

Solid understanding of RAG architecture: vector stores, chunking strategies, embedding models, reranking, eval loops

Experience with CI/CD, Terraform, and AWS in a production engineering context

Nice-to-Have

Experience building and shipping agentic systems in production: multi-step tool-calling agents, orchestration pipelines (LangGraph, LangChain, AutoGen, CrewAI, or custom)

Familiarity with agentic coding CLI tools and workflow automation that encodes team patterns at the repo level

Knowledge of MCP (Model Context Protocol) and experience building or integrating MCP servers

Experience with code intelligence: AST parsing, static analysis, code graph construction

Background in developer platform or internal tooling engineering

Experience with eval frameworks: RAGAS, LangSmith, Braintrust, or custom evaluation infrastructure

Familiarity with event-driven architectures, message queuing (Kafka, SQS), and distributed systems patterns

Experience with data platforms, ETL pipelines, or personalization systems at scale

Active contributions to open source or technical community leadership

Mindset

You treat repetitive engineering work as a problem to solve, not a cost of doing business

Your first instinct when facing toil is: can an agent handle this?

You stay current on model releases, agent design patterns, and emergin