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
â¢Develop and maintain the enterprise AI roadmap aligned with business objectives.
â¢Evaluate new AI technologies, frameworks, and vendors to support innovation.
â¢Define governance frameworks for responsible AI, privacy, security, and ethics.
â¢Lead AI/ML initiatives across multiple business units.
2. Architecture & Technical Design
â¢Design scalable, secure, cloud-native AI/ML architectures (Azure AI, AWS, GCP).
â¢Define MLOps frameworks for continuous training, deployment, monitoring, and lifecycle management.
â¢Architect data pipelines, vector databases, LLM orchestration, and retrieval-augmented generation (RAG) systems.
â¢Select appropriate models (LLMs, CV, NLP, Generative AI, predictive analytics) based on business needs.
3. Solution Development
â¢Provide technical leadership for building and deploying AI applications.
â¢Work with data scientists, ML engineers, and software teams to deliver production-grade models.
â¢Optimize AI workloads for cost, performance, and scalability.
â¢Oversee integration of AI into products, platforms, and enterprise systems.
4. Stakeholder Collaboration
â¢Translate business challenges into AI use cases with measurable outcomes.
â¢Work with product owners, data teams, and business leaders to prioritize initiatives.
â¢Present AI strategy and technical recommendations to executives and leadership teams.
5. Risk, Compliance & Responsible AI
â¢Ensure compliance with data protection laws (GDPR, HIPAA, DPDP, etc.).
â¢Create explainability and transparency frameworks for AI decisions.
â¢Implement controls to prevent bias, model drift, and data misuse.
Requirements
Required Skills & Experience:
Technical Skills
â¢12â15+ years of overall experience, with 4+ years in AI/ML architecture or leadership roles.
â¢Strong understanding of:
oMachine learning, deep learning, NLP, LLMs, RAG, transformers.
oCloud platforms: Azure AI, AWS Sagemaker, or Google Vertex AI.
oMLOps tools: MLflow, Databricks, Kubeflow, Airflow, Docker, Kubernetes.
oData engineering: Spark, Databricks, Data Factory, pipelines, ETL/ELT.
oProgramming: Python, SQL; familiarity with TensorFlow/PyTorch.
â¢Experience designing enterprise-grade AI systems and microservices architectures.
Soft Skills
â¢Strong communication and stakeholder-management skills.
â¢Ability to balance technical depth with strategic thinking.
â¢Leadership experience with cross-functional teams.
Preferred Qualifications
â¢Masterâs or bachelorâs degree in Computer Science, AI, Data Science, or related fields.
â¢Certifications in cloud (Azure AI Engineer, AWS ML Specialty, etc.).
â¢Experience implementing generative AI and LLM solutions in production.
â¢Background in industry-specific domains (finance, telecom, retail, healthcare, etc.).
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