Sr. GenAI Tooling Engineer

Job Title: Sr. GenAI Tooling Engineer

Location: Edina, MN (Hybrid - 3 days WFO)

Employment Type: C2H post 12 months

Job Description:-

Experience: - 12+ Years

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.