Senior GenAI Tooling Engineer

Role: Senior GenAI Tooling Engineer
Location: - Chicago , IL (Hybrid - 3 days WFO)
Experience: - 12+ Years
Duration: - 6 months +

Educational Qualifications: -

• Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.

Our Client is 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.

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

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.

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 must have 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.