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.
Robust 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.

📌 AI Architect (Bareilly)
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