AI Architect

AI Architect

Role Overview

The AI Architect / AI Lead will be responsible for defining the AI strategy, designing scalable AI/ML architectures, and leading end-to-end implementation of AI solutions across the organization. This role involves deep technical expertise, strategic leadership, and collaboration with cross-functional teams to drive the adoption of AI responsibly and effectively.

Key Responsibilities

1. AI Strategy & Leadership

Develop and maintain the enterprise AI roadmap aligned with business objectives.

Evaluate new AI technologies, frameworks, and vendors to support innovation.

Define governance frameworks for responsible AI, privacy, security, and ethics.

Lead AI/ML initiatives across multiple business units.

2. Architecture & Technical Design

Design scalable, secure, cloud-native AI/ML architectures (Azure AI, AWS, GCP).

Define MLOps frameworks for continuous training, deployment, monitoring, and lifecycle management.

Architect data pipelines, vector databases, LLM orchestration, and retrieval-augmented generation (RAG) systems.

Select appropriate models (LLMs, CV, NLP, Generative AI, predictive analytics) based on business needs.

3. Solution Development

Provide technical leadership for building and deploying AI applications.

Work with data scientists, ML engineers, and software teams to deliver production-grade models.

Optimize AI workloads for cost, performance, and scalability.

Oversee integration of AI into products, platforms, and enterprise systems.

4. Stakeholder Collaboration

Translate business challenges into AI use cases with measurable outcomes.

Work with product owners, data teams, and business leaders to prioritize initiatives.

Present AI strategy and technical recommendations to executives and leadership teams.

5. Risk, Compliance & Responsible AI

Ensure compliance with data protection laws (GDPR, HIPAA, DPDP, etc.).

Create explainability and transparency frameworks for AI decisions.

Implement controls to prevent bias, model drift, and data misuse.

Required Skills & Experience

Technical Skills

915+ years of overall experience, with 4+ years in AI/ML architecture or leadership roles.

Strong understanding of:

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Machine learning, deep learning, NLP, LLMs, RAG, transformers.

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Cloud platforms: Azure AI, AWS Sagemaker, or Google Vertex AI.

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MLOps tools: MLflow, Databricks, Kubeflow, Airflow, Docker, Kubernetes.

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Data engineering: Spark, Databricks, Data Factory, pipelines, ETL/ELT.

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Programming: Python, SQL; familiarity with TensorFlow/PyTorch.

Experience designing enterprise-grade AI systems and microservices architectures.

Soft Skills

Strong communication and stakeholder-management skills.

Ability to balance technical depth with strategic thinking.

Leadership experience with cross-functional teams.

Preferred Qualifications

Master’s or bachelor’s degree in Computer Science, AI, Data Science, or related fields.

Certifications in cloud (Azure AI Engineer, AWS ML Specialty, etc.).

Experience implementing generative AI and LLM solutions in production.

Background in industry-specific domains (finance, telecom, retail, healthcare, etc.).

What You Will Lead

Enterprise AI strategy & architecture

AI platform modernization & MLOps

GenAI and LLM adoption

Cross-functional AI squads

Innovation and PoCs

Responsible AI & governance