Lead AI Platform Architect (MLOps & GenAI)

Job Summary

We are seeking an experienced AI Architect to design, build, and scale enterprise-grade AI systems and platforms. The ideal candidate has successfully set up multiple AI systems end-to-end, translating business needs into robust AI architectures that are secure, scalable, and production-ready. This role bridges strategy, architecture, and hands-on implementation across data, infrastructure, and machine learning components.

Key Responsibilities

* Architect, design, and implement end-to-end AI platforms, from data ingestion through model deployment and monitoring

* Lead the setup of multiple AI systems, including model training pipelines, inference services, and MLOps frameworks

* Define AI reference architectures, best practices, and design standards across teams

* Partner with business stakeholders to translate use cases into technical AI solutions

* Select and integrate AI/ML tools, frameworks, and cloud services (e.g., model hosting, feature stores, vector databases)

* Establish scalable and secure MLOps practices, including CI/CD for models, versioning, monitoring, and retraining

* Guide teams on responsible AI, governance, explainability, and compliance requirements

* Evaluate emerging AI technologies and recommend platform improvements or innovations

* Provide technical leadership and mentorship to data scientists, ML engineers, and developers

Required Skills & Experience

* Proven experience as an AI Architect, ML Architect, or similar role

* Demonstrated success setting up multiple AI systems or platforms in production environments

* Strong understanding of:

* Machine learning and deep learning architectures

* Data pipelines, feature engineering, and model lifecycle management

* Cloud-native AI services and containerized deployments

* Hands-on experience with AI/ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)

* Experience designing APIs and services for model inference

* Solid grasp of security, scalability, and performance considerations for AI systems

* Ability to communicate complex technical concepts to non-technical stakeholders

Preferred Qualifications

* Experience with generative AI, LLMs, or retrieval-augmented generation (RAG) architectures

* Prior ownership of enterprise AI platforms or centers of excellence

* Familiarity with data governance, model risk management, and AI compliance standards

* Background in regulated industries (e.g., telecom, finance, healthcare, public sector)

* Cloud certifications or advanced degrees in Computer Science, AI, or related fields