GenAI Tooling Engineer

Role: Senior GenAI Tooling Engineer

Experience: - Min 12+ Years

Location: - Chicago, IL (Preferred Location) 3 days WFO per week

Seeking a Senior GenAI Tooling Engineer with expertise in GenAI, LLMs, OpenAI, Azure AI, Agentic AI, RAG Pipelines, Python, Amplitude, and Jellyfish to drive enterprise AI tooling strategy, governance, implementation, platform adoption, and engineering productivity across a regulated environment.

Roles and Responsibilities:

AI Tool Strategy & Portfolio Evolution

Evaluate emerging AI engineering tools and recommend platforms that improve engineering productivity, AI quality, governance, observability, and operational excellence.

Conduct technical assessments, proof of concepts, and platform evaluations.

Support business cases, platform roadmaps, and tool rationalization efforts.

Recommend enhancements that maximize engineering value while minimizing platform complexity.

Platform Implementation & Integration

Lead implementation, configuration, and lifecycle management of enterprise AI engineering platforms.

Initially own Jellyfish and Amplitude implementations, integrations, upgrades, and enterprise rollout.

Integrate platforms with Azure DevOps, GitHub, Jira, ServiceNow, Azure, identity services, RBAC, REST APIs, telemetry, and enterprise systems.

Develop reusable onboarding playbooks, automation, templates, and implementation standards.

Support engineering teams and applications during onboarding.

Platform Adoption & Engineering Enablement

Develop onboarding processes, documentation, training, and self-service capabilities.

Partner with engineering teams to maximize platform adoption and engineering productivity.

Drive change management activities and continuously improve developer experience.

Platform Success & Operations

Monitor platform health, availability, utilization, and operational performance.

Coordinate incident management, vendor escalations, upgrades, release planning, and maintenance.

Optimize platform configuration, licensing, performance, scalability, and operational maturity.

Automate repetitive platform administration activities wherever practical.

Engineering Analytics & Insights

Design and develop engineering dashboards, executive scorecards, operational KPIs, adoption metrics, utilization analytics, ROI dashboards, and business-value reporting.

Provide actionable insights that improve engineering effectiveness, platform investments, and decision making.

Analyse engineering trends and identify opportunities to improve platform usage and productivity.

Platform Optimization & Continuous Improvement

Continuously evaluate new capabilities and recommend platform enhancements.

Optimize integrations, workflows, licensing, feature adoption, and operational processes.

Develop reusable engineering assets that improve implementation speed and consistency.

Business Partnership

Partner with AI Engineering, AI Automation, AI QE, AI AppOps, Enterprise Architecture, Security, Cloud Engineering, Product teams, and Vendors.

Collaborate with AI Infrastructure & Cloud and Enterprise Data & Analytics Platform teams to ensure seamless integrations while respecting ownership boundaries.

Educational Qualifications: -

Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

Experience in implementing, integrating, administering, or supporting enterprise software platforms.

Strong experience implementing and supporting engineering productivity platforms such as Jellyfish, Amplitude, or comparable enterprise tools.

Experience integrating enterprise platforms using APIs, webhooks, SSO, RBAC, cloud services, and automation.

Experience onboarding engineering teams and applications to enterprise platforms.

Experience building engineering dashboards, executive scorecards, operational KPIs, and adoption analytics.

Strong scripting and automation skills (Python, PowerShell, APIs, automation workflows).

Excellent communication, consulting, troubleshooting, stakeholder management, and customer success skills.

Technical Skills & Technologies

The ideal candidate has strong hands-on experience across many of the following technology areas:

Engineering Productivity Platforms: Jellyfish, Amplitude, Azure DevOps, GitHub, Jira

AI-DLC, AI-QE & AI AppOps: LangSmith, Promptfoo, LangFuse, Arize, Phoenix, AI observability and evaluation platforms

Integration & Automation: REST APIs, Webhooks, Python, PowerShell, JSON, enterprise integrations

Cloud & Identity: Microsoft Azure, Azure OpenAI, SSO, RBAC, identity integration

Engineering Analytics: Power BI or similar visualization platforms, engineering scorecards, KPIs, operational dashboards, adoption analytics

Engineering Practices: SDLC, Agile, DevSecOps, release management, platform operations, continuous improvement

Organizational Boundaries

Owns:

AI Engineering productivity platforms

AI-DLC, AI-QE, AI AppOps, AI Observability, and AI Governance tools

Platform implementation, integration, onboarding, adoption, operations, optimization, and engineering analytics

Partners With:

AI Infrastructure & Cloud teams

Enterprise Data & Analytics Platform teams

Enterprise Architecture, Security, Product, and Engineering organizations

Success Measures

Rapid onboarding of engineering teams and applications.

High platform adoption, customer satisfaction, and feature utilization.

Reliable platform operations, availability, and operational maturity.

Actionable engineering dashboards and executive insights.

Optimized licensing, integrations, platform performance, and engineering productivity.

Continuous evolution of the AI engineering tooling ecosystem.

Skills

GenAI Amplitude, Jellyfish, LLM, OpenAI, Azure, Python RAG Pipeline, AgenticAI 'AI Tooling Strategy & Roadmap.

VeeRteq Solutions is an Equal Opportunity Employer