Lead Enterprise AI Architect

This role is for one of our clients

Industry: Software Development
Seniority level: Mid-Senior level

Min Experience: 6 years
Location: Bengaluru
JobType: full-time

We are looking for a Lead Enterprise AI Architect to design and operationalize mission-critical AI systems for large enterprise customers. This is a deeply technical, delivery-focused role centered on building secure, scalable, and production-ready AI solutions that solve real business problems.
You will work directly with customer engineering and data teams to translate complex operational challenges into robust AI architectures. From system design to live deployment, you will own the full lifecycle—ensuring solutions are reliable, explainable, and aligned with enterprise standards.
This role is best suited for engineers who thrive in high-accountability environments, enjoy architecting real-world systems, and prefer measurable impact over experimentation-only work.

Key Responsibilities
AI Architecture & Production Delivery
Design end-to-end enterprise AI architectures spanning application, model, and infrastructure layers.
Lead implementation of production-grade systems, including integration, orchestration, and backend services.
Ensure deployments meet enterprise expectations for availability, security, compliance, and performance.
Troubleshoot complex cross-layer issues across data, model behavior, and infrastructure components.
Advanced AI Systems & Intelligent Workflows
Build and deploy LLM-powered workflows, agent-based systems, and retrieval-augmented reasoning architectures.
Customize AI frameworks to align with customer-specific data ecosystems and compliance needs.
Architect modular, extensible systems that can scale across teams and use cases.
Data Engineering & Platform Integration
Design robust data pipelines covering ingestion, transformation, storage, and real-time serving.
Integrate AI systems with modern data platforms and streaming technologies for high-throughput workloads.
Implement data governance, lineage tracking, and explainability layers within AI pipelines.
Prototype to Scale Execution
Develop rapid proof-of-value implementations to validate business impact.
Transition prototypes into hardened, scalable production systems.
Maintain a balance between speed of execution and operational rigor.
Enterprise Collaboration & Advisory
Partner closely with customer engineering, product, and leadership teams.
Lead technical workshops and architectural discussions.
Translate AI capabilities into tangible business outcomes and implementation roadmaps.
Provide architectural guidance aligned with best practices and long-term scalability.
Governance, Monitoring & Responsible AI
Embed monitoring, observability, and drift detection into AI systems.
Design explainable and auditable workflows to meet compliance standards.
Support enterprise readiness for governance reviews and regulatory requirements.
Knowledge Leadership
Mentor customer teams through reviews, architecture guidance, and best-practice enablement.
Contribute deployment insights back into internal engineering standards and reusable frameworks.
Elevate engineering quality through documentation and operational discipline.

Candidate Profile
Required Experience
5+ years of experience building and deploying large-scale AI, ML, or distributed systems.
Strong software engineering foundation with systems-level design expertise.
Proficiency in Python, JavaScript, or Java with strong backend and distributed systems experience.
Hands-on experience deploying LLM-based applications, generative AI systems, or intelligent automation solutions.
Deep understanding of enterprise data platforms, system integrations, and production infrastructure.
Prior experience in customer-facing architecture or technical consulting roles.
Preferred Exposure
Cloud-native deployments across AWS, GCP, or Azure.
Kubernetes, containerization, or MLOps pipelines.
AI monitoring, observability, and governance tooling.
Experience deploying conversational AI or agent-based enterprise workflows.
Familiarity with responsible AI frameworks and compliance-driven implementations.

Work Structure
Hybrid model based in Bengaluru (2 days per week in office).
Up to 25% travel for customer engagements.

What This Role Offers
Direct ownership of enterprise AI implementations from concept to production.
Exposure to complex, high-scale business environments.
Influence over technical direction across customer ecosystems.
Opportunity to work at the convergence of advanced AI research and real-world system architecture.

Core Competencies
Enterprise AI Architecture · Production AI Systems · LLM & Agent-Based Workflows · Distributed Systems Engineering · Data Platform Integration · AI Governance & Observability · Customer-Facing Technical Leadership · Cloud & Infrastructure Engineering