Sr AI Engineer- Voice & Agentic AI

The Senior AI Engineer
will design, build, and ship production-grade AI systems, with a focus on real-time, agentic, and voice applications where reliability and latency are non-negotiable. This person owns AI features end to end: from agent orchestration and retrieval through deployment, evaluation, and monitoring in live environments. They work autonomously with minimal supervision while collaborating closely inside an embedded engineering team, and are expected to raise risks early, communicate proactively, and ship to a high bar.

The heart of this role is making intelligent systems work in the real world, where an agent answering a customer has to respond fast, stay accurate, and never give a confidently wrong answer. You will bridge AI engineering and production software: building agent workflows, fast and accurate retrieval, clean APIs, and the evaluation discipline that keeps it all trustworthy as it scales across customers.

The heart of this role is making intelligent systems work in the real world, where an agent answering a customer has to respond fast, stay accurate, and never give a confidently wrong answer. You will bridge AI engineering and production software: building agent workflows, fast and accurate retrieval, clean APIs, and the evaluation discipline that keeps it all trustworthy as it scales across customers.

What are the main responsibilities of this role?

• Design and build LLM-powered agents and multi-step workflows using orchestration frameworks (Lang Graph or equivalent), with explicit, auditable state.
• Build retrieval (RAG) systems that are both accurate and fast enough to meet strict real-time latency budgets; making deliberate, defensible tradeoffs between recall and response time.
• Own AI features end to end: from prototype to production deployment, monitoring, and iteration.
• Refactor and improve live production systems without disrupting active customers.
• Build and maintain evaluation and regression harnesses so changes are validated against scenarios, not demos; and bugs are caught before they reach production.
• Create scalable Python APIs and services that expose AI functionality to web, voice, and mobile clients.
• Implement observability, tracing, and performance monitoring across deployed AI features — tracking latency, quality, and cost.
• Work across a multi-LLM stack (OpenAI, Anthropic, Google) behind a provider-abstraction layer, keeping model choice a configuration decision rather than a rewrite.
• Collaborate daily in a remote, sprint-based team, picking up scoped work, reviewing PRs through quality gates, and surfacing risks and blockers early.

• 4+ years in AI/ML or AI-focused software engineering, with real products shipped to production.
• Hands-on experience building LLM applications and agents (OpenAI, Claude, open-source models) using agent/orchestration frameworks (Lang Graph, Lang Chain, or equivalent).
• Strong RAG and retrieval experience — embeddings, vector databases (Pinecone, Weaviate, pgvector, or similar), and the practical skill of tuning retrieval for both quality and speed.
• Solid Python, with experience building production APIs (FastAPI or similar).
• Experience evaluating and testing AI systems (RAGAS, Maxim, Promptfoo, LLM-as-judge, or equivalent) and a "done means tested, not demoed" mindset.
• Production experience on cloud (AWS preferred) with Docker, CI/CD, and infrastructure-as-code (Terraform).
• Strong software fundamentals: automated testing, code review, version control (Git), and modular design.
• Demonstrated experience deploying, monitoring, and refactoring AI systems running in production.
• AI-native development workflow (Cursor, Claude Code, Copilot) and the…