Senior AI Deployment Engineer - Client-Facing Production Systems

Scrums.com; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

Scrums.com seeks a senior engineer to lead customer-facing deployments of applied AI, from identifying business value to building and launching reliable software in production.

The role blends technical discovery, hands-on engineering, architecture, and operational ownership, coordinating delivery across customer teams, software, infrastructure, and AI agents, while raising the deployment team's technical standard.

Qualifications

• 6+ years of professional software engineering experience, including production systems.
• Advanced Python and strong TypeScript or JavaScript production language skills.
• Hands‑on AI/ ML or data‑intensive product deployment in production.
• Strong systems design across APIs, SQL/NoSQL, cloud, security, and observability.
• Knowledge of LLM patterns: retrieval-augmented generation, tools, evaluations, agent orchestration.
• Experience guiding technical discovery with senior stakeholders and independent delivery.

Responsibilities

• Turn discovery with business and technical stakeholders into sequenced plans with measurable outcomes.
• Build production AI systems, agent workflows, data pipelines, integrations, and supporting services.
• Configure agent context, tools, permissions, evaluations, observability, and safeguards.
• Make architecture decisions across models, retrieval, APIs, data stores, cloud services, and customer systems.
• Define evaluation suites and production measures for quality, reliability, security, latency, adoption, and cost.
• Coordinate releases and handover, resolve complex production issues, lead incident response, mentor engineers, and communicate trade-offs and risks.

Skills

Advanced Python

TypeScript

JavaScript

AI deployment

APIs design

SQL/NoSQL

Cloud infrastructure

Security

Observability

LLM patterns

Technical discovery

Job description

Scrums.com seeks a senior engineer to lead customer-facing deployments of applied AI, from identifying business value to building and launching reliable software in production.

The role blends technical discovery, hands-on engineering, architecture, and operational ownership, coordinating delivery across customer teams, software, infrastructure, and AI agents, while raising the deployment team's technical standard.