Why This Role Stands Out
This AI Platform Engineer role at Stellar IT Solutions offers significant opportunities for technical growth and impact within a reputable tech company, complemented by a flexible hybrid work model. You'll thrive here if you're a mid-senior level engineer passionate about building cutting-edge AI infrastructure and collaborating within a dynamic team. Apply today to shape the future of AI!
Quick Overview
Job Description
Job Title: AI Platform Engineer
Job Location: Hybrid (3 days onsite, 2 days remote), Chicago, IL
Travel: Occasional international travel required
Job Duration: Long Term Contract
Job Summary:
We are seeking an AI Platform Engineer to build and scale enterprise AI platform capabilities that enable multiple product teams. This role focuses on developing production-ready AI solutions, creating reusable platform components, establishing governance and quality standards, and enabling AI adoption across the organization.
Required Skills:
- Strong Python development experience.
- Hands-on experience building and deploying production-grade AI/LLM or Agentic AI solutions.
- Experience evaluating AI systems and maintaining quality for non-deterministic applications.
- Strong software engineering fundamentals with production deployment experience.
- Systems-level thinking with the ability to build reusable platform capabilities rather than single-product solutions.
- Enablement mindset with experience supporting engineering teams through common patterns, tooling, and best practices.
Preferred Skills:
- Experience with Agentic AI or multi-agent workflows.
- Intelligent Document Processing (IDP) experience.
- Background in AI platform engineering, AI enablement, or AI architecture.
- Experience with .NET, React, or Angular is a plus.
- Background as a Data Scientist with strong engineering skills or a Full Stack Engineer with AI experience.
Key Responsibilities:
- Build reusable AI platform components, tooling, and guardrails for multiple product teams.
- Define AI governance, evaluation frameworks, and quality standards.
- Enable engineering teams through shared AI capabilities, documentation, and training.
- Partner with architects to standardize AI development practices across the organization.
- Support the rollout of SDLC and AI governance processes while driving platform adoption.
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