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Company Overview
A global professional services organization is seeking an AI Data Engineer Manager to lead the design, development, and delivery of AI/ML/GenAI solutions. The organization focuses on transforming workforce and business processes by combining deep technical expertise with strategic insights. It helps clients solve complex challenges through innovative data-driven solutions, enabling secure, scalable, and high-impact AI applications.
Summary
The AI Data Engineer Manager will lead data architecture and engineering efforts to enable AI/ML/GenAI solutions. This role ensures that data pipelines are secure, observable, and scalable while supporting large language models (LLMs) and machine learning applications. The position blends hands-on technical leadership, delivery management, and team development, working closely with cross-functional stakeholders to implement production-ready solutions.
Responsibilities
Lead the end-to-end design and delivery of AI/ML/GenAI architectures, from data ingestion to model deployment
Operationalize modern data and retrieval foundations to support LLM-powered applications, including RAG, embeddings, vector search, and governed access to structured and unstructured data
Manage day-to-day engineering delivery with onshore/offshore teams
Partner with data science, ML engineering, and product teams to translate business use cases into scalable pipelines and platforms
Ensure strong data governance, lineage, quality controls, and monitoring
Define AI/ML technical direction and vision aligned with organizational goals
Select and implement appropriate open-source and commercial technologies, integrating cloud and on-premises systems
Contribute to MLOps and LLMOps practices for operational deployment of models
Conduct R&D to scale AI/ML-powered features, balancing quality, performance, and cost
Collaborate with multiple teams to pilot use cases and align technical implementation with business requirements
Serve as a technical advisor on AI trends, business impacts, and implementation best practices
Implement agile methodologies for AI solution delivery and drive continuous improvement
Mitigate risks and ensure ethical AI implementation, including compliance with regulations
Build tools for data ingestion, feature engineering, and distributed computing solutions to ensure scalable, secure, and reliable machine learning infrastructure
Qualifications
Bachelor’s degree in Computer Science, Statistics, Data Science, Information Systems, or related field
6+ years of consulting experience leading delivery teams, including onshore and offshore team members
6+ years of experience gathering non-functional requirements and defining application architecture frameworks
5+ years of experience in AI environments and translating requirements into client-ready design documents
5+ years of software application architecture analysis, design, and delivery
Experience executing full system development life cycle implementations
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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