Job Description & How to Apply Below
You'll architect scalable AI solutions, guide technical teams through complex implementations, and balance cutting-edge innovation with practical business outcomes.
Job Description :
We 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.
Key Responsibilities
Design, build, and orchestrate multi-agent systems capable of autonomous decision-making and task execution.
Lead the development and deployment of Generative AI solutions using LLMs and fine-tuned models.
Implement RAG (Retrieval-Augmented Generation) pipelines with robust document parsing, re-ranking, and context optimization.
Integrate AI agents into enterprise systems through APIs, function calling, and workflow orchestration frameworks (e.g., Lang Graph, CrewAI, Llama Index, Haystack).
Fine-tune and evaluate LLMs (using LoRA, PEFT, or QLoRA) for domain-specific use cases.
Collaborate cross-functionally with data, platform, and Dev Ops teams to ensure scalable and secure AI deployments.
Ensure production-grade quality performance optimization, monitoring, and continuous improvement.
Provide technical mentorship to junior engineers (minimal team handling required).
Required Skills & Experience
7
-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.
Strong background in Machine Learning, Deep Learning , and Python programming .
Hands-on experience with LLM frameworks (Lang Chain, Llama Index, Haystack, Semantic Kernel, etc.).
Proficiency in multi-agent orchestration (CrewAI, Lang Graph, Swarm, Autogen, or custom frameworks).
Expertise in vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) and embedding models .
Proven fine-tuning experience using LoRA, QLoRA, or PEFT.
Experience in enterprise-grade GenAI implementations — from PoC to production.
Strong understanding of RAG architecture , document chunking , context optimization , and model evaluation .
Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Excellent problem-solving and debugging skills.
About Us
Grid 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 & Dev Ops, application modernization and customer experience.
Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.
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