AI Ops Consultant

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 organizations 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)

Disclaimer : This job posting has been aggregated from external source. Role detailscontentand availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.