About Us: We are a leading consulting firm specializing in AI and digital transformation. Our mission is to help large enterprises harness the power of AI to drive innovation and achieve their business goals.
Role Purpose: Design and lead the technical foundation for enterprise AI initiatives, ensuring scalable, secure, and production-ready architectures that enable organizations to realize measurable business value from AI investments while maintaining governance and operational excellence.
Key Outcomes & Responsibilities
AI Architecture & Solution Design
- Define enterprise AI reference architectures that enable rapid experimentation, scalable deployment, and long-term maintainability
- Design end-to-end AI solutions spanning data ingestion, model development, deployment, and monitoring layers
- Ensure architectural decisions balance innovation with enterprise standards for security, performance, and cost optimization
- Create reusable patterns, templates, and accelerators that reduce time-to-value for AI implementations
Data Architecture & MLOps Foundation
- Architect data ecosystems that provide high-quality, accessible, and governed data for AI/ML workloads
- Design MLOps pipelines enabling continuous integration, delivery, and monitoring of AI models at scale
- Establish feature stores, model registries, and versioning frameworks that ensure reproducibility and traceability
- Optimize data architectures for real-time inference, batch processing, and hybrid deployment scenarios
Platform & Infrastructure Strategy
- Define cloud-native AI platform strategies leveraging Azure, AWS, Google Cloud Platform, or hybrid environments
- Evaluate and recommend AI/ML platforms, tools, and frameworks aligned with client capabilities and objectives
- Design infrastructure architectures that optimize compute, storage, and networking for AI workloads
- Ensure platform choices support scalability, cost efficiency, and future technology evolution
AI Governance & Security Architecture
- Embed governance, security, and compliance requirements into AI architecture from design phase
- Design model explainability, bias detection, and fairness monitoring capabilities into solution architectures
- Architect audit trails, lineage tracking, and access controls that meet regulatory requirements
- Ensure architectures support responsible AI principles including transparency, accountability, and privacy
Technical Leadership & Stakeholder Alignment
- Translate business requirements into technical architectures that stakeholders across business and IT can understand
- Guide cross-functional teams including data engineers, data scientists, and DevOps through implementation
- Influence enterprise architecture decisions to ensure AI readiness across technology landscape
- Serve as technical authority on AI initiatives, resolving design conflicts and ensuring architectural integrity
Innovation & Technology Roadmap
- Evaluate emerging AI technologies (GenAI, LLMs, edge AI) and assess applicability to client contexts
- Define technology roadmaps that evolve AI capabilities in alignment with business strategy
- Lead proof-of-concepts and technical pilots to validate architectural approaches before scale
- Contribute to intellectual property through reusable assets, frameworks, and technical publications
Preferred Skills
- Enterprise AI Architecture – Proven ability to design scalable, production-grade AI architectures that support diverse use cases from experimentation through enterprise-wide deployment with measurable business impact
- Data Engineering & MLOps Mastery – Deep expertise in architecting data pipelines, feature stores, model registries, and MLOps frameworks that enable continuous delivery and monitoring of AI models at scale
- Cloud & Platform Expertise – Extensive hands-on experience with cloud-native AI services across Azure (Azure ML, Synapse, Databricks), AWS (SageMaker, Bedrock), or Google Cloud Platform (Vertex AI) with ability to design hybrid and multi-cloud solutions
- Technical Leadership & Influence – Strong ability to lead technical teams, resolve complex design challenges, and communicate architectural decisions effectively to both technical and business stakeholders
- Governance & Security Integration – Experience embedding responsible AI principles, security controls, and regulatory compliance requirements into AI architectures from design through deployment
Qualifications:
- 10-15 years of overall IT experience with minimum 8+ years focused on data engineering, AI/ML, and solution architecture
- 5+ years of experience designing and delivering enterprise-scale AI/ML and GenAI solutions in production environments
- Demonstrated track record of leading architecture for AI transformation programs across multiple industries
- Experience working with Fortune 500 or large enterprise clients in consulting or technology delivery roles
- Knowledge of AI Governance and Responsible & Ethical AI practices.
- MBA preferred.
Why Join Us:
- Opportunity to work with leading experts in the field of AI.
- Engage in challenging and innovative projects with top-tier clients.
- Competitive salary and benefits package.
• • Supportive and collaborative work environment.
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