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
- 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.
- 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.
- 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.
- 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.
- 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.
Requirements
Required Skills & Experience:
Technical Skills
- 12–15+ years of overall experience, with 4+ years in AI/ML architecture or leadership roles.
- 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.
- 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.).
