AI Engineer

Services For The Underserved, Inc.•New York, NY

1d•$150,000 - $175,000•Onsite

About The Position

The AI Engineer builds the standards, tools, and controls that help S:US move AI solutions from experimentation into reliable use. Reporting to the Vice President, Apps, Data & AI, this role partners across IT to deliver secure, reusable, and cost-effective AI capabilities that support the S:US mission.

Requirements

Five or more years of professional software engineering experience in cloud-based environments.
Hands-on experience building agentic AI solutions, including agent workflows, prompt design, and tool or API integrations.
Strong knowledge of Microsoft Azure and experience with GitHub Actions or Azure DevOps.
Experience building secure CI/CD pipelines with automated testing, security scanning, and quality checks.
Experience evaluating AI output and applying responsible AI, privacy, and security controls.
Strong full-stack engineering, API integration, automated testing, version control, and code review skills.
Working knowledge of observability and monitoring tooling (Azure Monitor, Application Insights) and delivery/issue-tracking platforms (e.g., Jira).
Excellent communication and collaboration skills, with the ability to turn emerging AI capabilities into practical solutions.

Nice To Haves

Experience with LLMOps or agent observability tooling, including tracing, token spend monitoring, and model routing/fallback, in a production setting.
Experience in a regulated enterprise technology environment, including direct exposure to PHI-handling or HIPAA compliance requirements.
Familiarity with Jira or similar modern agile delivery tooling used to plan, track, and orchestrate engineering work.
Experience standing up an AI or platform engineering capability from the ground up, ideally as an early or founding technical hire.

Responsibilities

Build and improve the AI development environment including GitHub, VS Code, GitHub Copilot, Azure, agents, and approved integrations.
Design reusable patterns for agent workflows, multi-agent coordination, MCP integrations, testing, and review.
Evaluate emerging AI development tools and recommend those that fit S:US’s Microsoft-first environment.
Integrate AI solutions with enterprise platforms and APIs so workflows operate reliably from end to end.
Build testing and evaluation practices for AI-generated output, including automated checks and human review for higher-risk changes.
Apply responsible AI, privacy, and security requirements to agent workflows and AI tools.
Protect PHI and other sensitive client, employee, and business data through appropriate access, masking, redaction, and approval controls.
Partner with IT Operations & Security to manage prompt injection, permissions, third-party tools, secrets, and software supply chain risks.
Build and strengthen CI/CD pipelines using GitHub Actions, Azure DevOps, and Azure.
Embed automated testing, security scanning, and quality checks into the software delivery process.
Monitor agent performance, reliability, latency, token usage, and cost.
Use performance and cost data to improve models, workflows, and platform decisions.
Partner with Applications, Data, Analytics, IT Infrastructure & Security, and PMO to apply shared AI standards and improve delivery.
Create practical playbooks, training, and office hours that help teams use AI engineering tools effectively.
Translate emerging AI capabilities into clear recommendations, standards, and practices that teams can adopt.