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

- 9-15+ years of overall experience, with 4+ years in AI/ML architecture or leadership

roles.

- Strong understanding of:

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

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

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

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

o 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