Role: AI Platform Engineer
Location: Chicago, IL (Hybrid, 1 week onsite every month)
Duration: Long Term
About the Role
Client is seeking an AI Platform Engineer to bridge our current cloud and DevOps operations with our next-generation AI-powered development platform. This is an individual contributor role on the AI & Cloud Operations team - you'll own the platform underpinning our DevOps practice (AKS, CI/CD, IaC, and operational excellence) while equally driving AI development pipeline strategy, MCP server infrastructure, and the modernization of our delivery toolchain and developing AI Agents using within various AI platforms such as Claude, Gemini, Sierra and DevRev.
This is a hands-on role with DevOps experience, cloud knowledge and previous AI Agent development and deployment.
What You'll Own
Key Responsibilities
Production Readiness Assessment
Receive prototype applications and conduct structured assessments covering security posture, data model integrity, authentication and authorization flows, input validation, dependency hygiene, and test coverage quality
Identify and document failure patterns endemic to AI-generated code including hardcoded secrets, flat or unindexed schemas, missing error handling, and hallucinated or unpinned dependencies
Produce clear remediation plans with prioritized findings, working within the architectural standards set by the Full-Stack Systems Architect
Hands on experience building Agentic Agents in Gemini/Vertex, OpenAI, Claude or similar tools
Code Remediation & Hardening
Refactor and harden AI-generated codebases to meet enterprise production standards across frontend frameworks, backend APIs, data modeling, and authentication systems
Replace or rewrite AI-generated test suites against human-reviewed acceptance criteria, ensuring coverage reflects real production behavior rather than checkbox validation
Use AI-augmented development tools (Cursor, Claude Code, GitHub Copilot) to accelerate remediation work while exercising independent judgment on when AI tooling is introducing new risk
Security & Compliance
Identify and remediate common security vulnerabilities including injection flaws, broken authentication, insecure direct object references, and exposed secrets or credentials
Implement and validate secure authentication and authorization patterns in accordance with enterprise security policies
Ensure applications meet CI/CD pipeline requirements and version control standards prior to production deployment
Pattern Recognition & Knowledge Management
Document recurring AI code failure patterns and contribute to a growing internal knowledge base
Feed pattern intelligence back upstream to improve prototype quality at the source, collaborating with developers and architects to reduce remediation burden over time
Stay current on AI-assisted development tooling, emerging failure modes, and production readiness best practices
Collaboration & Communication
Partner with application teams, architects, and business stakeholders to align on readiness criteria and timelines
Communicate technical findings clearly to both engineering and non-technical audiences
Provide guidance and thought leadership on responsible use of AI development tools within the engineering organization
Qualifications
Core Engineering
Strong full-stack fundamentals across at least one major frontend framework (React, Vue, Angular), backend API development, relational data modeling, and authentication systems
Proficiency in Python, JavaScript/TypeScript, and at least one additional backend language
Solid understanding of RESTful API design, database schema design, and ORM patterns
Experience with version control discipline, branching strategies, and code review processes
AI Code Failure Pattern Recognition
Strong ability to identify AI-generated code failure modes: hardcoded credentials, hallucinated libraries, flat schemas, checkbox tests, missing error handling, and over-reliance on happy-path logic
Practical experience evaluating AI tool output for correctness, security, and production viability
Ability to distinguish between AI tooling as an accelerant versus AI tooling compounding a problem
Security & Production Standards
Familiarity with OWASP Top 10 and common application security vulnerabilities
Experience implementing or validating secure authentication flows (OAuth 2.0, JWT, session management)
Understanding of CI/CD pipeline requirements, environment configuration, and secrets management
Testing & Quality
Experience writing and reviewing test suites with meaningful coverage - unit, integration, and end-to-end
Ability to evaluate test quality and replace AI-generated checkbox tests with coverage that reflects real production behavior
Communication & Collaboration
Strong written and verbal communication skills with the ability to present technical findings to non-technical stakeholders
Proven ability to work both independently and within cross-functional engineering teams
Self-starter with strong problem-solving skills and a bias toward documentation and knowledge sharing
Education & Experience
Bachelor's degree in computer science, Information Systems, or a related field; equivalent professional experience considered
5+ years of full-stack software development experience
3+ years of hands-on experience with AI-augmented development tools in a professional context (Cursor, Claude Code, GitHub Copilot, or equivalent)
2+ years of experience in application security, code review, or production engineering disciplines
Demonstrated experience identifying and remediating vulnerabilities in production codebases
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