•The AI Platform Engineer is responsible for designing, building, and maintaining the scalable infrastructure and tooling that enable efficient development, deployment, and monitoring of AI and machine learning solutions across the enterprise.
•This role develops and manages end-to-end AI platforms, including data pipelines, model training environments, MLOps frameworks, CI/CD automation, containerization, cloud infrastructure, and monitoring systems. It ensures reliability, scalability, security, and performance optimization of AI workloads while aligning with enterprise architecture and governance standards.
•Strategically, the AI Platform Engineer accelerates AI adoption by providing standardized, reusable, and production-ready environments that reduce time-to-market for AI initiatives. The role strengthens operational stability, model lifecycle governance, and cross-team collaboration—ensuring AI capabilities are sustainable, secure, and enterprise-grade
Requirements
•Experience (YoE): 6–10 years total; 3–5+ years in AI platform / MLOps within large-scale environments
•Education: Bachelor’s in Computer Science / Engineering / Data; cloud or DevOps certifications (AWS/Azure/GCP) preferred
•Project Exposure: Led end-to-end AI platform builds (data pipelines, ML deployment, CI/CD, monitoring) with production-scale impact
•Company Coverage: Experience in large enterprises, tech companies, or consulting with multi-business / regional exposure
•Delivery Scope & Scale: Proven track record supporting multiple AI use cases, high data volumes, and enterprise-grade systems
Summary
The AI Platform Engineer designs, builds, and maintains scalable infrastructure and tooling for AI/ML solutions, including data pipelines, MLOps, CI/CD, and cloud infrastructure. Requires 6-10 years total experience with 3-5+ years in AI platform/MLOps in large-scale environments and a Bachelor's in Computer Science, Engineering, or Data.
Responsibilities
Designing, building, and maintaining scalable infrastructure and tooling for AI/ML solutions; developing and managing end-to-end AI platforms including data pipelines, model training environments, MLOps frameworks, CI/CD automation, containerization, cloud infrastructure, and monitoring systems; ensuring reliability, scalability, security, and performance optimization; accelerating AI adoption through standardized, reusable, production-ready environments; strengthening operational stability, model lifecycle governance, and cross-team collaboration
Qualifications
Experience (YoE): 6–10 years total; 3–5+ years in AI platform / MLOps within large-scale environments; Education: Bachelor's in Computer Science / Engineering / Data; cloud or DevOps certifications (AWS/Azure/GCP) preferred; Project Exposure: Led end-to-end AI platform builds with production-scale impact; Company Coverage: Experience in large enterprises, tech companies, or consulting with multi-business / regional exposure; Delivery Scope & Scale: Proven track record supporting multiple AI use cases, high data volumes, and enterprise-grade systems
Education requirements
Bachelor's in Computer Science / Engineering / Data
Experience requirements
6–10 years total; 3–5+ years in AI platform / MLOps within large-scale environments