AI Architect

AI System Architect

Location: Bangalore

Total yrs of exp : 8 to 12 yrs

Np- Immediate Joiners

JD:

ROLE SUMMARY

We are looking for an experienced Senior AI System Architect who can design scalable, secure, and production-grade AI-powered platforms from the ground up. This role goes beyond traditional cloud architecture - the candidate must be equally fluent in modern AI system design, including LLM infrastructure, Retrieval-Augmented Generation (RAG) pipelines, agentic workflow orchestration, and vector search systems.

The primary focus will be architecting - an enterprise-grade AI-powered customer support system - along with other AI product initiatives. You will own end-to-end architecture decisions across the application, AI inference, data, integration, cloud infrastructure, and operational reliability layers, while guiding engineering teams to build and evolve these systems.

KEY RESPONSIBILITIES

• Architect end-to-end AI-powered systems including LLM inference pipelines, RAG architectures, multi-agent orchestration, and conversational AI backends for production scale.
• Design vector search and knowledge management infrastructure - selecting vector databases (OpenSearch, Pinecone, Weaviate, pgvector), chunking strategies, and embedding pipelines.
• Define LLM serving patterns: model routing, fallback chains, streaming responses, prompt caching, token budgeting, and latency optimization.
• Architect cloud-native solutions on AWS using services such as EC2, ECS/EKS, Lambda, S3, RDS, DynamoDB, API Gateway, SQS/SNS, Amazon Bedrock, SageMaker, CloudWatch, IAM, and VPC.
• Own architecture across application, infrastructure, data, integration, and deployment layers; build design documents, trade-off analyses, and technical roadmaps.
• Define standards for microservices, CI/CD, observability, resiliency, disaster recovery, and AI governance (prompt injection prevention, PII handling, content filtering, audit logging).
• Evaluate technology choices and make recommendations based on scalability, performance, security, cost, and maintainability; drive modernization and cloud migration initiatives.
• Guide and mentor engineering teams through implementation; review architectures, ensure standards are followed, and troubleshoot complex production issues.
• Work closely with product, DevOps, security, and business stakeholders to translate requirements into technical designs, and communicate decisions to both technical and non-technical audiences.

REQUIRED SKILLS

• Proven experience designing production GenAI systems: RAG pipelines, LLM orchestration layers, agentic workflows, and vector search infrastructure.
• Hands-on AWS architecture experience across core and AI/ML services: Amazon Bedrock (Knowledge Bases, Agents), SageMaker inference endpoints, Lambda-based ML serving, and standard AWS compute, storage, networking, and security services.
• Strong understanding of distributed systems, microservices, REST/gRPC APIs, and event-driven architecture.
• Experience with Kubernetes, Docker, CI/CD pipelines, and infrastructure-as-code (Terraform or CloudFormation).
• Familiarity with vector databases (OpenSearch, Pinecone, Weaviate, FAISS, or pgvector) and LLM infrastructure patterns: streaming, batching, context window management, and cost optimization.
• Knowledge of AI-specific security and compliance: PII/PHI handling in AI pipelines, prompt injection prevention, content filtering, guardrails, and compliance logging.
• Strong knowledge of SQL and NoSQL databases, cloud security (IAM, VPC, encryption, secrets management), and production observability.
• Excellent communication skills - ability to articulate architectural decisions to both engineering teams and non-technical stakeholders.