AI Architect

You'll architect scalable AI solutions, guide technical teams through complex implementations, and balance cutting-edge innovation with practical business outcomes.\n\nJob Description :\nWe are looking for an experienced AI Architect with hands-on expertise in Generative AI and Agentic AI to design, develop, and deploy production-grade, enterprise-scale AI solutions . The ideal candidate should have strong proficiency in Python, Machine Learning , and multi-agent system orchestration , with proven experience delivering end-to-end implementations with minimal supervision.\nKey Responsibilities\nDesign, build, and orchestrate multi-agent systems capable of autonomous decision-making and task execution.\nLead the development and deployment of Generative AI solutions using LLMs and fine-tuned models.\nImplement RAG (Retrieval-Augmented Generation) pipelines with robust document parsing, re-ranking, and context optimization.\nIntegrate AI agents into enterprise systems through APIs, function calling, and workflow orchestration frameworks (e.g., LangGraph, CrewAI, LlamaIndex, Haystack).\nFine-tune and evaluate LLMs (using LoRA, PEFT, or QLoRA) for domain-specific use cases.\nCollaborate cross-functionally with data, platform, and DevOps teams to ensure scalable and secure AI deployments.\nEnsure production-grade quality performance optimization, monitoring, and continuous improvement.\nProvide technical mentorship to junior engineers (minimal team handling required).\n\nRequired Skills & Experience\n7 -15 years of total experience, with Min 3+ years in Generative AI and 1+ years Agentic AI with Min 2 or 3 Production grade implementation at Enterprise Level.\nStrong background in Machine Learning, Deep Learning , and Python programming .\nHands-on experience with LLM frameworks (LangChain, LlamaIndex, Haystack, Semantic Kernel, etc.).\nProficiency in multi-agent orchestration (CrewAI, LangGraph, Swarm, Autogen, or custom frameworks).\nExpertise in vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) and embedding models .\nProven fine-tuning experience using LoRA, QLoRA, or PEFT.\nExperience in enterprise-grade GenAI implementations — from PoC to production.\nStrong understanding of RAG architecture , document chunking , context optimization , and model evaluation .\nFamiliarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).\nExcellent problem-solving and debugging skills.\n\nAbout Us\nGrid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation.

A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.