job summary:

Randstad Digital is actively seeking a Capability Lead to join our team.

Capability building, solutions design, AI, ML, Intelligent Operations, AIOps, MLOps, Cloud, Go-to-market strategy is required.

Job Duties –

1. Strategic & Offering Development

• -Lead AI-Driven Service Strategy: Lead the strategic initiative to embed intelligent operations and AI solutions across our entire service portfolio.

• -Develop New Service Offerings: Collaborate with global stakeholders to design, package, and bring to market new, high-impact consulting and managed service offerings focused on AI-driven business value and operational efficiency.

• -Develop and execute end-to-end Go-to-Market (GTM) strategies for new AI/ML service offerings.

• -Create and manage a scalable sales enablement program, equipping sales and solution architect teams with the collateral, training, and tools to effectively sell AI solutions.

• -Lead the development and curation of the practice’s Intellectual Property (IP), including reusable code accelerators, standardized SOWs, and delivery frameworks.

• -Lead AI Partner Strategy: Identify, select, and manage the AI partner ecosystem for key vendors for embedded AI technologies and AI for Operations toolsets.

• -Act as a trusted advisor to client executives, nurturing senior relationships to identify and shape new opportunities for strategic account growth.

• -Contribute to Financial Modeling: Partner with leadership to develop financial models, client-facing ROI calculators, and business value assessments for new and existing offerings.

• -Define Practice KPIs: Define and report on the Key Performance Indicators (KPIs) for the practice, including solution ROI, client adoption, and operational efficiency gains.

2 Technical & Architectural Leadership

• -Define Technical Architectures: Establish and govern the reference architectures for AIOps (AI for IT Operations), MLOps, cloud-native AI platforms, and Generative AI solutions.

• -Operationalize AI: Build and enforce best practices for the entire machine learning lifecycle (MLOps), including data pipelines, model training, CI/CD, deployment, monitoring, and governance.

• -Establish Responsible AI Governance: Champion and implement the practice’s “Responsible AI” framework, ensuring ethical, transparent, and compliant deployment of all client and internal solutions.

• -Drive Innovation: Stay at the forefront of AI/ML advancements, driving experimentation and rapid prototyping of new solutions to solve complex client problems.

• -Ensure Production Stability: Implement and oversee robust monitoring systems to ensure the performance, reliability, and production-grade stability of all deployed AI/ML models.

3. Team & Practice Leadership

• -Build & Mentor Teams: Recruit, lead, and mentor AI engineers, MLOps specialists, and data scientists. Foster a culture of innovation, collaboration, and continuous learning.

• -Cross-Functional Collaboration: Partner closely with other capability leads to embed AI capabilities and ensure security is integrated into all solutions.

• -Evangelize & Educate: Act as the primary internal and external evangelist for the practice’s AI capabilities, communicating value to technical and non-technical stakeholders.

LI-NL1 #LI-Remote

location: Telecommute

job type: Solutions

salary: $75 – 80 per hour

work hours: 9am to 5pm

education: Bachelors

responsibilities:

Job Duties –

1. Strategic & Offering Development

• -Lead AI-Driven Service Strategy: Lead the strategic initiative to embed intelligent operations and AI solutions across our entire service portfolio.

• -Develop New Service Offerings: Collaborate with global stakeholders to design, package, and bring to market new, high-impact consulting and managed service offerings focused on AI-driven business value and operational efficiency.

• -Develop and execute end-to-end Go-to-Market (GTM) strategies for new AI/ML service offerings.

• -Create and manage a scalable sales enablement program, equipping sales and solution architect teams with the collateral, training, and tools to effectively sell AI solutions.

• -Lead the development and curation of the practice’s Intellectual Property (IP), including reusable code accelerators, standardized SOWs, and delivery frameworks.

• -Lead AI Partner Strategy: Identify, select, and manage the AI partner ecosystem for key vendors for embedded AI technologies and AI for Operations toolsets.

• -Act as a trusted advisor to client executives, nurturing senior relationships to identify and shape new opportunities for strategic account growth.

• -Contribute to Financial Modeling: Partner with leadership to develop financial models, client-facing ROI calculators, and business value assessments for new and existing offerings.

• -Define Practice KPIs: Define and report on the Key Performance Indicators (KPIs) for the practice, including solution ROI, client adoption, and operational efficiency gains.

2 Technical & Architectural Leadership

• -Define Technical Architectures: Establish and govern the reference architectures for AIOps (AI for IT Operations), MLOps, cloud-native AI platforms, and Generative AI solutions.

• -Operationalize AI: Build and enforce best practices for the entire machine learning lifecycle (MLOps), including data pipelines, model training, CI/CD, deployment, monitoring, and governance.

• -Establish Responsible AI Governance: Champion and implement the practice’s “Responsible AI” framework, ensuring ethical, transparent, and compliant deployment of all client and internal solutions.

• -Drive Innovation: Stay at the forefront of AI/ML advancements, driving experimentation and rapid prototyping of new solutions to solve complex client problems.

• -Ensure Production Stability: Implement and oversee robust monitoring systems to ensure the performance, reliability, and production-grade stability of all deployed AI/ML models.

3 Team & Practice Leadership

• -Build & Mentor Teams: Recruit, lead, and mentor AI engineers, MLOps specialists, and data scientists. Foster a culture of innovation, collaboration, and continuous learning.

• -Cross-Functional Collaboration: Partner closely with other capability leads to embed AI capabilities and ensure security is integrated into all solutions.

• -Evangelize & Educate: Act as the primary internal and external evangelist for the practice’s AI capabilities, communicating value to technical and non-technical stakeholders.

qualifications:

• 10 years of experience in technology, with at least 5 years in a senior leadership role focused on AI, Machine Learning, or Data Science.

• -Proven track record of building and shipping AI/ML products or services from concept to production.

• -Deep technical expertise in defining and implementing architectures for AIOps, MLOps, and cloud-native AI platforms (AWS, Azure, or Google Cloud Platform).

• -Proven experience designing and executing GTM strategies for new technology services.

• -Demonstrated ability to build and maintain C-level relationships and drive account expansion (upsell/cross-sell) within existing clients.

• -Experience building and leading high-performing technical teams.

• -Strong client-facing and communication skills, with the ability to act as a strategic advisor to C-level executives.

• -Strong business acumen, with experience in developing pricing models, ROI justifications, and measuring the business value of technology solutions.

• -Deep understanding of AI ethics, data privacy regulations, and model governance frameworks.

• -Bachelor’s degree in Computer Science, Engineering, Statistics, or equivalent experience.

Desired Skills & experience –

• Hands-on experience with Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architectures.

• -Proficiency with common data science and ML tools (e.g., Python, SQL, TensorFlow, PyTorch, Kubeflow).

• -Expertise in major ITSM platforms and a deep understanding of ITIL processes (incident, problem, change management).

• -Deep familiarity with modern Observability and monitoring platforms (e.g., Dynatrace, Datadog, Splunk) and data sources (logs, metrics, traces).

• -Experience with IT automation and orchestration tools (e.g., Ansible, Terraform) to enable ‘closed-loop’ remediation.

• -Experience with conversational AI, intelligent automation, or agentic AI platforms to automate ITSM and business processes.

• -Experience with FinOps principles and cloud financial management, including the cost optimization and forecasting of AI toolsets.

• -Understanding of SecOps, including the integration of AIOps with SIEM/SOAR platforms for correlated threat and operational event analysis.

• -Experience in building and productizing reusable assets, frameworks, and accelerators for a consulting or services organization.

• -Experience in a consulting or professional services environment, with a track record of developing service offerings and supporting presales.

• -Master’s or PhD in a relevant field.

• -Published research