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