Enterprise Architect + AI Focus

Enterprise Architect - AI Focus

Experience Required: 10-16 years overall IT experience; 4-6+ years in AI/ML architecture or advanced analytics platforms; strong enterprise integration experience

1. Background

As organizations evaluates and scales AI and advanced analytics capabilities, TCS supports assessment and advisory initiatives to ensure AI adoption is secure, governed, and aligned with enterprise architecture standards.

The Enterprise Architect (AI Focus) provides architectural leadership to assess AI readiness, define future-state AI architecture, and integrate AI capabilities into the broader enterprise ecosystem.

2. Skills Required

Experience designing enterprise-scale AI/ML architectures and integrating AI services into enterprise applications

Knowledge of LLM/GenAI concepts, RAG patterns, and model selection trade-offs

Understanding of MLOps lifecycle concepts: reproducibility, CI/CD for ML, monitoring, and drift

Knowledge of data pipelines and data readiness for AI (structured/unstructured)

Experience with cloud AI services (AWS/Azure/GCP) and secure integration patterns

Understanding of orchestration/event patterns (queues, triggers, APIs) to embed AI into workflows

Familiarity with responsible AI governance concepts (auditability, policy guardrails)

Awareness of PII/regulated data boundaries and access controls for AI workflows

Ability to define logging/audit and approval gates for AI deployments

Ability to translate AI opportunities into measurable outcomes and architecture roadmaps

Strong documentation and executive communication skills

3. Responsibilities

Drive discovery and business alignment: identify AI use cases aligned with business goals; assess feasibility (technical, legal, ethical) and define measurable outcomes

Design end-to-end AI architecture patterns including AI integration with enterprise apps, event flows, orchestration, and AI input/output pipelines

Define data readiness and knowledge retrieval patterns (e.g., RAG) where appropriate; guide data boundaries and access controls

Define security, compliance, and auditability controls for AI: authentication, rate limiting, prompt/response logging, and retention strategies

Define model management guardrails: model selection guidance, versioning, evaluation, release governance, and rollback strategies

Establish production AI lifecycle architecture with MLOps principles: automation, monitoring, drift detection, reproducibility, and operational readiness

Partner with enterprise architecture/security teams to ensure AI initiatives integrate cleanly with platform governance

Produce executive-ready AI architecture artifacts: target state, inte gration patterns, governance model, and phased adoption roadmap

Salary Range- $120,000-$150,000 a year