AI Data Engineer - Manager
Required Skills
Job Description
**AI Data Engineer \- Manager**
Our Human Capital practice is at the forefront of transforming the nature of work. As converging forces reshape industries, our team uniquely addresses the complexities of work, workforce, and workplace dynamics. We leverage sector\-specific insights and cross\-domain perspectives to help organizations tackle their most challenging workforce issues and align talent strategies with their strategic visions. Our practice is renowned for making work better for humans and humans better at work. Be part of this exciting era of change and join us on this transformative journey.
Recruiting for this role ends on August 30, 2026
**Work You'll Do:**
The **AI Data Engineer** will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM\-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data. You will manage day\-to\-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production\-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands\-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
**Strategic Alignment and Vision**\* Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
* Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases
**Architectural Design**\* Design end\-to\-end AI architectures, from data ingestion to model deployment, integrating with cloud and on\-premises systems.
**Design and Technology Selection**\* Select appropriate technologies from a pool of open\-source and commercial offerings, considering deployment models and integration with existing tools.
* Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
**Research and Development**\* Conduct research to provide technical solutions to scale AI/ML powered features for real\-world challenges, making trade\-offs based on quality, scalability, performance, and cost.
* Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
**Collaboration and Stakeholder Engagement**\* Collaborate with Enterprise, Application, Data \& DevOps teams, Data scientists, Machine Learning \& GenAI Engineers, and Business teams to pilot use cases and discuss best design.
* Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
**Consulting \& Advisory**\* Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
**Operational Excellence and Continuous Improvement**\* Be responsible for the successful execution of AI\-powered applications using agile methodology.
* Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
* Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
**Risk Management and Ethical Considerations**\* Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
* Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
**Product Strategy and Business Understanding**\* Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
* Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
**Tool Development and Data Management**\* Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
* Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
**The Team**
Our Insights, Innovation \& Operate Offering is designed to enhance key aspects o
Our Human Capital practice is at the forefront of transforming the nature of work. As converging forces reshape industries, our team uniquely addresses the complexities of work, workforce, and workplace dynamics. We leverage sector\-specific insights and cross\-domain perspectives to help organizations tackle their most challenging workforce issues and align talent strategies with their strategic visions. Our practice is renowned for making work better for humans and humans better at work. Be part of this exciting era of change and join us on this transformative journey.
Recruiting for this role ends on August 30, 2026
**Work You'll Do:**
The **AI Data Engineer** will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM\-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data. You will manage day\-to\-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production\-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands\-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
**Strategic Alignment and Vision**\* Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
* Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases
**Architectural Design**\* Design end\-to\-end AI architectures, from data ingestion to model deployment, integrating with cloud and on\-premises systems.
**Design and Technology Selection**\* Select appropriate technologies from a pool of open\-source and commercial offerings, considering deployment models and integration with existing tools.
* Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
**Research and Development**\* Conduct research to provide technical solutions to scale AI/ML powered features for real\-world challenges, making trade\-offs based on quality, scalability, performance, and cost.
* Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
**Collaboration and Stakeholder Engagement**\* Collaborate with Enterprise, Application, Data \& DevOps teams, Data scientists, Machine Learning \& GenAI Engineers, and Business teams to pilot use cases and discuss best design.
* Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
**Consulting \& Advisory**\* Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
**Operational Excellence and Continuous Improvement**\* Be responsible for the successful execution of AI\-powered applications using agile methodology.
* Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
* Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
**Risk Management and Ethical Considerations**\* Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
* Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
**Product Strategy and Business Understanding**\* Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
* Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
**Tool Development and Data Management**\* Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
* Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
**The Team**
Our Insights, Innovation \& Operate Offering is designed to enhance key aspects o
Posted: 2026-03-21