Role Overview
We are building a dedicated Enterprise GenAI Operations function to maximize the business value of enterprise AI platforms while ensuring sustainable adoption, operational
excellence, governance, and cost efficiency.
As an Enterprise GenAI Operations Consultant, you will play a key role in helping establish and mature this capability by analysing AI platform usage, driving adoption initiatives,
optimizing operational costs, and providing strategic recommendations to engineering leadership.
Working at the intersection of AI Operations, Technology Consulting, Data Analytics, Engineering Productivity, and AI Governance, you will collaborate with engineering teams, platform
owners, finance, and business stakeholders to ensure enterprise AI investments deliver measurable business outcomes.
This role is ideal for professionals with experience in technology consulting, business analytics, engineering operations, FinOps, platform operations, or enterprise AI
operations who enjoy solving business problems using data-driven insights.
Key Responsibilities
Enterprise GenAI Operations
Help establish and mature the Enterprise GenAI Operations function.
Define operational processes, governance frameworks, KPIs, and reporting standards.
Develop repeatable operational playbooks for sustainable enterprise AI adoption.
Support leadership in improving the organization's AI operational maturity.
AI Usage Analytics & Insights
Analyse enterprise-wide usage of GenAI platforms including Claude Code, GitHub Copilot, Codex, ChatGPT Enterprise, Gemini, and other AI-assisted productivity tools.
Monitor AI adoption, user engagement, utilization trends, token consumption, request volumes, model usage, and platform health.
Collect and analyse AI platform telemetry to identify trends across engineering teams, business units, projects, and user personas.
Build executive dashboards and operational reports that provide actionable business insights.
Translate AI usage data into strategic recommendations for engineering leadership.
AI Cost Optimization
Analyse enterprise AI platform spending and identify opportunities for cost optimization.
Recommend strategies for optimizing token consumption, licensing models, subscription plans, context utilization, and model selection.
Identify inactive users, underutilized licenses, inefficient AI usage patterns, and optimization opportunities.
Develop repeatable AI cost optimization frameworks and continuous monitoring processes.
Partner with stakeholders to improve AI return on investment while maintaining developer productivity.
Engineering Productivity
Measure AI adoption effectiveness across engineering teams.
Analyse how AI tools contribute to engineering productivity and software delivery.
Identify opportunities to improve developer experience while balancing operational costs.
Recommend best practices that maximize engineering efficiency through responsible AI adoption.
Operational Excellence
Monitor operational KPIs related to AI adoption, utilization, productivity, governance, and cost efficiency.
Identify operational bottlenecks affecting AI platform adoption.
Recommend process improvements that improve enterprise AI operations.
Support continuous operational improvement initiatives across AI platforms.
Stakeholder Engagement & Consulting
Partner with engineering leaders, product teams, platform owners, finance, and business stakeholders to understand AI usage patterns.
Conduct workshops, interviews, and discovery sessions to identify optimization opportunities.
Facilitate AI adoption reviews and operational governance meetings.
Present executive-level dashboards, business insights, and strategic recommendations.
Build trusted relationships across technical and business teams.
Governance & Responsible AI
Promote responsible AI usage across the organization.
Define AI operational standards, governance models, and usage guidelines.
Support compliance with enterprise security, governance, and responsible AI policies.
Maintain documentation of AI operational processes, governance frameworks, and optimization recommendations.
Support enterprise-wide AI operational maturity initiatives.
Continuous Improvement
Stay current with emerging GenAI platforms, enterprise AI pricing models, and AI operational best practices.
Evaluate new AI capabilities and recommend opportunities that improve productivity while optimizing operational costs.
Benchmark enterprise AI adoption against industry trends.
Continuously improve AI operational processes and reporting capabilities.
Required Qualifications
Bachelor’s degree in computer science, Information Technology, Engineering, or a related field.
5–8 years of experience in Technology Consulting, Business Analytics, Engineering Operations, Platform Operations, FinOps, Enterprise AI Operations, or related disciplines.
Experience analysing large enterprise datasets and deriving actionable business insights.
Strong analytical, consulting, and problem-solving skills.
Excellent stakeholder management and executive communication skills.
Experience developing dashboards using Power BI,Tableau, Looker, or similar analytics platforms.
Strong SQL skills for data analysis.
Advanced Microsoft Excel skills for reporting and analytics.
Experience working with APIs and structured data sources.
Ability to translate operational data into executive-level recommendations.
Experience defining KPIs, operational metrics, and performance dashboards.
Preferred Qualifications
Experience working with enterprise AI platforms such as Claude Code, GitHub Copilot, Codex, ChatGPT Enterprise, Gemini, Cursor, or similar AI developer tools.
Strong understanding of Large Language Model (LLM) concepts including:
Token consumption.
Context windows.
Model capabilities.
Inference costs.
Model selection strategies.
Experience with AWS, Microsoft Azure, or Google Cloud Platform.
Knowledge of Python for analytics, reporting automation, and operational workflows.
Familiarity with FinOps principles and cloud cost optimization.
Experience measuring engineering productivity and developer experience.
Experience working within Agile software delivery environments.
Exposure to enterprise governance, operational maturity models, or digital transformation initiatives.
Success Metrics The successful candidate will be measured on:
Reduction in enterprise AI platform costs through optimization initiatives.
Improvement in AI adoption across engineering and business teams.
Increased utilization of enterprise AI licenses and subscriptions.
Identification and resolution of inefficient AI usage patterns.
Quality, accuracy, and business impact of optimization recommendations.
Timely delivery of executive dashboards and operational reporting.
Improvement in AI governance and operational maturity.
Increased stakeholder satisfaction across engineering and business teams.
Demonstrable improvements in developer productivity enabled by AI.
Measurable return on enterprise AI investments (AI ROI).
Why Join JKTech
You will have the opportunity the help shape and build a new Enterprise GenAI Operations capability from the ground up. Working closely with engineering leadership and business stakeholders, you will influence how AI is adopted, governed, and optimized across the organization while helping maximize the business value of enterprise AI investments.
