AI Architect

Role Overview

We are looking for an experienced AI Architect / Technical Lead to own the technical vision and execution of AI systems across the organization. This is a senior, hands-on leadership role responsible for designing scalable AI architectures, guiding engineering teams, and ensuring successful deployment of AI solutions from concept to production.

The role requires minimum 6 years of AI-focused experience, covering generative AI, classical machine learning, and production-grade ML systems.

Key Responsibilities

Architecture & Strategy

1. Design end-to-end, production-ready AI system architectures including data ingestion, model training, inference, APIs, and monitoring.
2. Define reference architectures for ML, deep learning, and generative AI solutions.
3. Establish standards for model evaluation, versioning, governance, and lifecycle management
4. Decide between managed AI services and custom-built implementations based on business needs
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Technical Leadership

1. Lead and mentor AI engineers, ML specialists, and data engineers
2. Conduct technical reviews of models, pipelines, and deployments
3. Define coding, documentation, and testing standards for AI projects
4. Align AI development with DevOps and engineering best practices

Collaboration & Integration

1. Work with DevOps teams on CI/CD, infrastructure, security, and MLOps
2. Collaborate with backend and frontend teams for AI service integration
3. Translate business requirements into technical AI solutions

Production Ownership

1. Ensure AI systems meet scalability, latency, reliability, and cost requirements
2. Define monitoring, alerting, and retraining strategies
3. Manage risks related to data drift, model degradation, failures, and compliance

Required Skills & Experience

• Strong expertise in machine learning, deep learning, and applied AI
• Proven experience in designing and deploying AI systems in production
• Advanced Python skills and proficiency in at least one backend language
• Experience with cloud platforms and distributed architectures
• Knowledge of MLOps tools, pipelines, and best practices
• Ability to make architectural trade-offs in real-world scenarios
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• What We Are Looking For
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• A technical authority who can own and defend AI architecture decisions
• Strong balance between AI theory and large-scale system design
• A hands-on leader who can guide teams without losing technical depth
• Experience delivering AI features used by real customers
• Practical mindset around cost, performance, and operational complexity
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This Role Is NOT

• A research-only position
• A people manager role without deep technical involvement