GEN AI Architect

Title- GEN AI Architect/ Agentic AI Developer (Python/LLM/RAG)

Required Skills:

• Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
• Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
• Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
• Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
• Experience with observability tools (Datadog, logging, tracing, metrics).
• Familiarity with Postgre
  
  SQL, DBT, data modeling, schema evolution, and performance tuning.
• Hands on experience on AI
• AI Engineer who has extensive knowledge on LLM creation, AI tool adoption and also defining the frameworks
• should be strongly vocal enough to communicate with the customers
• Design, develop, and maintain LLM-powered multi-agent workflows for code analysis, remediation proposals, and safe patch generation.
• Implement agentic patterns including planning/execution loops, dynamic tool orchestration, sandboxing, guardrails, and failure recovery.
• Build scalable automation systems for technical debt remediation: language/runtime upgrades, vulnerability patching, dependency modernization, and config drift correction.
• Partner with Dev Experience and Platform teams to define engineering guidelines and reusable standards across the organization.
• Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, managing chunking, embeddings, hybrid search, reranking, and retrieval policies.
• Develop robust evaluation frameworks for LLMs, RAG, and agent workflows, including offline datasets, validation metrics, statistical testing, and A/B tests.
• Contribute to backend systems using Python, distributed systems, microservices, Postgre
  
  SQL, DBT, vector databases, caching, streaming, and queueing.
• Build CI/CD pipelines, observability dashboards, and perform performance analysis on model, retrieval, and network layers.
• Collaborate cross-functionally with product, platform, and security to move prototypes to production-grade services.
• Communicate clearly with stakeholders, write technical documentation, and mentor junior engineers.