Role Overview :

We are seeking a senior AI Architect to define the enterprise AI strategy, architecture, and implementation roadmap across intelligent applications, machine learning platforms, generative AI solutions, and AI-enabled business processes.

Key Responsibilities :

- Define the enterprise AI architecture vision, technology standards, and roadmap aligned with business objectives.

- Architect end-to-end AI solutions spanning data ingestion, data preparation, model development, deployment, inference, monitoring, and continuous improvement.

- Design scalable machine learning platforms and production-grade AI applications for enterprise use cases.

- Define generative AI architectures using large language models, foundation models, retrieval-augmented generation (RAG), and agentic workflows.

- Evaluate AI models, platforms, frameworks, and deployment approaches based on accuracy, latency, scalability, cost, security, and business value.

- Establish architecture patterns for model serving, feature management, vector search, embeddings, prompt management, and AI application integration.

- Define MLOps and LLMOps practices covering model versioning, evaluation, deployment automation, monitoring, and lifecycle management.

- Design secure AI integration with enterprise applications, APIs, knowledge repositories, data platforms, and business workflows.

- Establish AI governance frameworks covering model risk, explainability, privacy, data lineage, auditability, and responsible AI practices.

- Define controls to mitigate hallucinations, prompt injection, data leakage, model misuse, and other AI-specific security risks.

- Evaluate and implement appropriate cloud AI services and platforms across AWS, Microsoft Azure, Google Cloud, and relevant AI ecosystems.

- Drive strategies for model selection, fine-tuning, inference optimization, and hybrid or private AI deployment.

- Establish AI evaluation frameworks covering model quality, groundedness, robustness, fairness, reliability, and operational performance.

- Collaborate with data engineering, data science, product, security, legal, and enterprise architecture teams.

- Identify and prioritize AI opportunities through feasibility assessments, proof-of-concept initiatives, and business-value analysis.

- Define reusable AI components, reference architectures, platform capabilities, and standards for organization-wide adoption.

- Guide engineering and data science teams in moving AI solutions from experimentation to production.

- Mentor senior AI engineers, machine learning engineers, data scientists, and architects.

Required Skills and Qualifications :

- 17 - 25 years of overall experience in technology, software architecture, data platforms, AI/ML engineering, or related disciplines.

- Strong understanding of machine learning, deep learning, model evaluation, inference, and production AI systems.

- Experience designing end-to-end AI/ML platforms and enterprise-scale intelligent applications.

- Strong understanding of generative AI architectures, LLM integration, RAG, embeddings, vector databases, and agentic systems.

- Experience with AI/ML platforms and frameworks such as AWS SageMaker, Azure Machine Learning, Google Vertex AI, PyTorch, or TensorFlow.

- Knowledge of Python-based AI ecosystems and integration with enterprise software platforms.

- Experience with MLOps/LLMOps, model registries, automated deployment, monitoring, and model lifecycle management.

- Strong understanding of data architecture, data quality, data governance, data privacy, and enterprise integration.

- Experience with cloud architecture, APIs, distributed systems, containers, and scalable model-serving infrastructure.

- Knowledge of AI security, responsible AI, model explainability, regulatory considerations, and governance frameworks.

- Ability to assess technical feasibility, operational complexity, cost optimization, and business impact of AI solutions.

- Strong communication skills and the ability to influence senior business and technology stakeholders.

Preferred Qualifications :

- Experience delivering enterprise generative AI, predictive analytics, intelligent automation, or AI-enabled decision-support platforms.

- Exposure to vector databases, model gateways, guardrails, AI evaluation frameworks, and inference optimization.

- Experience defining AI governance standards and enterprise-wide AI adoption strategies.

- Relevant cloud AI, machine learning, or architecture certifications.

Key Competencies :

- AI strategy, enterprise AI architecture, generative AI, machine learning platforms, responsible AI, MLOps/LLMOps, and strategic technology leadership.

Updated CTC :

- 80LPA - 1.2Crore