AI Platform Engineer

Job summary

Meet the Moment with Alt*** We're living through a once-in-a-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision - and Alt*** is leading the transformation. You'll be working on the challenges that sit at the heart of modern business.

Benefits

Qualifications

• Experience in solution engineering, platform engineering, enterprise applications, cloud, integration, automation, identity, data, or related technology roles.
• Hands-on familiarity with LLM platforms, APIs, agents, RAG, workflow automation, or AI tooling.
• Understanding of enterprise security, privacy, access control, governance, and audit readiness.
• Ability to translate business use cases into secure, scalable, reusable solution patterns.
• Strong communication
• Experience establishing cost management, usage tracking, agent visibility, or AI oversight infrastructure.
• Experience creating technical standards, reference architectures, production readiness checklists, or support playbooks.
• Success Measures
• Success in this role will be measured by consistently evaluated and prioritized AI use cases, reusable and secure approved AI patterns, governed adoption of AI platforms and
• and you share our values, we encourage you to apply.
• As part of our ongoing c ommitment to a diverse, equitable, and inclusive workplace, we're invested in building teams with a wide variety of backgrounds, identities, and experiences.
• Find yourself checking a lot of these boxes but doubting whether you should apply?

Responsibilities

• You'll be working on the challenges that sit at the heart of modern business.
• Configure and support enterprise AI platforms, including OpenAI, Anthropic, Google, and related AI tooling.
• Define approved access patterns for LLMs, APIs, MCP, agents, connectors, RAG, automation, and human-in-the-loop workflows.
• Partner with business teams to assess AI use cases, clarify outcomes, evaluate feasibility, and recommend the right solution pattern.
• Lead or co-lead lighthouse AI projects that prove reusable enterprise patterns and demonstrate measurable business value.
• Create solution blueprints for priority AI use cases, including architecture, data needs, access patterns, risk, cost, support model, and production readiness.
• Establish cost management, usage tracking, agent visibility, and oversight infrastructure.
• Define evaluation approaches for AI solutions, including quality checks, test cases, usage metrics, human review points, failure handling, and improvement loops.
• Package successful pilots into reusable reference architectures, implementation templates, and transition plans for production support.
• Partner with security, privacy, legal, architecture, data, and IT teams on governance, risk review, and standards.
• Advise business leads and citizen developers on when AI solutions can remain team-owned versus when they require enterprise ownership, controls, monitoring, and support.
• Work with the AI Enablement Analyst to convert platform capabilities into training, documentation, FAQs, playbooks, and support processes.

Skills

Degrees

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