Senior AI Architect

Key Responsibilities\n\nDesign and implement scalable AI/ML and Generative AI architectures for enterprise applications.\nDevelop LLM-powered applications, autonomous AI agents, and multi-agent orchestration systems.\nArchitect state management and persistent memory systems for long-running AI workflows.\nBuild and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.\nLead the evaluation, selection, and implementation of AI technologies, frameworks, and infrastructure.\nCollaborate with stakeholders to define business requirements and convert them into technical solutions.\nDesign and implement AI evaluation, observability, and monitoring frameworks.\nDevelop scalable APIs, microservices, and distributed AI systems.\nDeploy and manage AI solutions on cloud platforms, primarily AWS.\nImplement MLOps/LLMOps practices for model lifecycle management and deployment automation.\nMentor engineering teams and bridge the gap between Data Science and ML Engineering teams.\nContribute to solution proposals, RFP responses, architecture documentation, and effort estimations.\nEnsure adherence to industry best practices, security standards, and performance engineering principles.\n\nRequired Skills & Qualifications\nAI & Machine Learning\nStrong expertise in Machine Learning, Generative AI, and Large Language Models (LLMs).\nHands-on experience designing and deploying LLM-based applications and agentic AI systems.\nExperience with multi-agent orchestration frameworks such as: LangGraph, CrewAI, AutoGen, Semantic Kernel, Strong understanding of: Prompt engineering, Embeddings, Vector databases, RAG architecture, Autonomous workflow design\nExperience implementing AI evaluation and monitoring frameworks.\nFamiliarity with ML pipelines and frameworks such as MLflow, Kubeflow, or similar platforms.\nStrong programming expertise in Python. Hands-on experience with: NumPy, Pandas, Scikit-learn\nExperience designing scalable microservices and distributed systems.\nStrong API development and integration experience.\nCloud & Infrastructure\nExperience deploying AI solutions on AWS.\nFamiliarity with Docker and Kubernetes.\nUnderstanding of AI infrastructure, vector databases, and data pipelines.\nExperience with MLOps and LLMOps platforms.\nArchitecture & Leadership\nExpertise in distributed systems architecture.\nStrong understanding of scalability, reliability, and performance engineering.\nAbility to design enterprise-grade AI platforms and frameworks.\nStrong technical leadership and mentoring capabilities.\nExcellent analytical, communication, and stakeholder management skills.\nAbility to explain complex AI concepts to both technical and non-technical audiences.\nStrong documentation and architecture communication skills.\nPreferred Qualifications\nYears Of Experience: 14 to 18 Years\nEducation/Qualification: BE / B.Tech / MCA / M.Tech\nExperience working on enterprise AI transformation initiatives.\nExposure to autonomous AI systems and workflow orchestration platforms.\nExperience contributing to RFPs, technical proposals, and solution estimations.\nProven track record of deploying AI solutions into production environments.