AI Architect (LLMs, RAG, Vertex AI)
Position : AI Architect
Experience Required : 12–15 Years
Location : Noida(Work from Office)
Employment Type : Full-Time
Shift : US Shift
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About the Role
We are seeking a highly skilled AI Architect with deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vertex AI platform to design, build, and deploy enterprise-grade AI solutions. The ideal candidate will have hands-on experience in building and fine-tuning open-source foundation models, integrating AI / ML pipelines within cloud ecosystems, and defining scalable AI architectures aligned to business needs.
This role requires a balance of technical innovation, architectural leadership, and practical implementation to accelerate enterprise AI adoption.
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Key Responsibilities
- Architect and lead the development of LLM-based solutions using open-source and proprietary models (Llama, Falcon, Mistral, GPT-J, etc.).
- Design and implement RAG frameworks for enterprise use cases combining vector databases, embeddings, and document stores.
- Leverage Vertex AI for model training, fine-tuning, monitoring, and lifecycle management.
- Build scalable AI pipelines and inference architectures using Python, TensorFlow, PyTorch, and LangChain frameworks.
- Develop prompt engineering and optimization strategies for model reliability and contextual accuracy.
- Collaborate with data engineers to design data pipelines for model training and evaluation.
- Integrate AI models with enterprise systems via APIs and microservices architectures.
- Define AI governance, ethical AI practices, and model performance KPIs.
- Mentor cross-functional teams in AI / ML model development, deployment, and MLOps practices.
- Evaluate new technologies, foundation models, and research to enhance AI platform capabilities.
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Required Skills & Experience
12–15 years of overall experience in software / AI engineering, with at least 5+ years in AI architecture and applied ML.Proven expertise in LLM development, fine-tuning, and RAG implementation using open-source frameworks.Strong experience with Google Vertex AI (Model Registry, Pipelines, Workbench, and Model Deployment).Proficiency in Python, TensorFlow, PyTorch, LangChain, Hugging Face Transformers.Hands-on experience with Vector Databases (Pinecone, Weaviate, Milvus, pgvector, FAISS).Familiarity with retrieval, embeddings (OpenAI, Vertex, Cohere, Hugging Face), and knowledge graphs.Deep understanding of MLOps pipelines, CI / CD for AI, and cloud-based ML lifecycle management.Experience integrating models with APIs, RESTful services, and microservices architectures.Strong grounding in AI model governance, bias detection, and ethical AI frameworks.________________________________________
Preferred Qualifications
Experience with multi-cloud AI architectures (AWS Sagemaker, Azure ML, GCP Vertex AI).Familiarity with GenAI orchestration frameworks (LangChain, LlamaIndex, DSPy).Contributions to open-source AI / ML repositories or model development communities.Certifications in AI / ML Engineering, Cloud AI Architecture (GCP Professional ML Engineer).Exposure to RAG-based enterprise chatbots or domain-specific LLM deployments.Think global. Think BIG.
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