Enterprise AI Systems Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Enterprise AI Systems Engineer based in the United States.

This role offers the opportunity to own the enterprise AI technology stack end to end, from platform administration and governance to infrastructure, integrations, and automation.
You will shape how AI tools are deployed and used across the organization, supporting a growing user base while balancing security, performance, cost, and adoption.
The position combines software engineering, cloud architecture, AI platform operations, and security in a highly hands-on environment.
You will build the infrastructure and connectivity that enable AI platforms to securely interact with internal systems and business workflows.
You will also develop agentic automations, evaluate models, and establish guardrails that make AI-powered solutions reliable and predictable.
Working closely with cybersecurity, AI governance, finance, and business stakeholders, you will translate complex technical requirements into scalable standards and practical solutions.
This is a high-ownership role for an engineer who enjoys building systems from the ground up and directly influencing enterprise AI strategy and operations.

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Accountabilities:

You will take end-to-end ownership of enterprise AI platforms and supporting infrastructure, ensuring they are secure, reliable, cost-effective, well-governed, and useful to teams across the organization.

• Administer enterprise AI and SaaS platforms, including Claude, Gemini, OpenRouter, Cursor, and NotebookLM, covering provisioning, groups, roles, configuration, and tenant security.
• Build and maintain skills, prompts, projects, connectors, and documentation that improve platform functionality and adoption.
• Drive adoption across a growing user base while monitoring platform usage and identifying opportunities to improve utilization.
• Track AI platform consumption and spending, identify idle licenses, and support business-unit chargeback processes.
• Monitor platform availability, manage feature rollouts, troubleshoot user issues, and own escalations when problems arise.
• Build and operate Model Context Protocol (MCP) servers and gateways that securely connect AI platforms with internal systems.
• Maintain the LLM gateway responsible for model routing, authentication, rate limiting, logging, and policy enforcement across providers.
• Evaluate and select models for specific workloads based on cost, latency, capabilities, and data sensitivity.
• Build agentic workflows and automations using internal APIs and tools, incorporating evaluation frameworks and guardrails for predictable behavior.
• Architect and operate AI workloads across AWS and GCP, including compute, networking, IAM, secrets management, private connectivity, Bedrock, and Vertex AI.
• Develop and maintain production code in GitHub, primarily using Python and TypeScript, while implementing branch protection, access controls, secret scanning, and CI/CD pipelines.
• Instrument the AI technology stack for cost, performance, and security telemetry and create reporting used by leadership and finance teams.
• Partner with Cybersecurity and AI Governance teams to enforce data handling, retention, access, and auditability requirements.
• Develop and maintain technical standards, documentation, and operational runbooks for enterprise AI systems.

Requirements:

The ideal candidate combines strong software, cloud, and platform engineering experience with hands-on expertise building and operating LLM-based systems. You should be comfortable working across infrastructure, AI platforms, security, and governance while communicating effectively with both technical and non-technical stakeholders.

• 5+ years of experience in software, cloud, or platform engineering, including recent hands-on experience building and operating LLM-based systems.
• Experience administering enterprise SaaS or AI platforms at scale, including access management, configuration, cost management, and adoption.
• Working knowledge of commercial and open-weight LLMs, including prompt engineering, evaluation, retrieval, and agentic architectures.
• Hands-on experience with Model Context Protocol (MCP), MCP gateways, LLM gateways, or comparable API integration and middleware technologies.
• Strong experience with AWS and GCP architecture, including IAM, networking, secrets management, and managed AI services.
• Experience with GitHub and modern engineering practices including version control, code review, testing, CI/CD, and infrastructure as code.
• Strong Python and/or JavaScript/TypeScript skills for building integrations, automations, and internal tooling.
• Understanding of AI security and data governance fundamentals, including least privilege, data classification, DLP, and auditability.
• Ability to explain technical concepts and decisions clearly to executives, finance teams, and other non-technical stakeholders.
• Bachelor’s degree in a relevant field preferred, or equivalent professional experience.
• Experience with Vertex AI or Amazon Bedrock is a plus.
• Familiarity with self-hosted open-weight model deployment, vector databases, RAG pipelines, Splunk or other SIEM platforms, and cloud cost-management tools is advantageous.

Benefits:

• Salary range of $126,000–$206,000 USD depending on location and labor market tier.
• Tier 1 locations: Greater New York City and San Francisco Bay Area — $165,000–$206,000.
• Tier 2 locations: Greater Boston, Washington DC, Seattle/Tacoma, Greater Sacramento, Southern California — $150,000–$187,000.
• Tier 3 locations include Austin, Dallas, Houston, Chicago, Atlanta, Miami, Colorado, Phoenix, Las Vegas, Portland, and other specified U.S. markets — $141,000–$176,000.
• Tier 4 locations: the rest of the United States, including Alaska and Puerto Rico — $126,000–$157,000.
• Medical, dental, and vision insurance.
• Disability and life insurance.
• Family support, wellness, legal, and employee assistance program benefits.
• 401(k) plan with company matching.
• Equity opportunities and performance-based bonuses.
• Biannual discretionary performance bonuses.
• Cell phone subsidy.
• Commuter benefits.
• Product discounts.
• Opportunities for professional growth, learning, and collaboration with experienced technology teams.

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How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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