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9.0 - 14.0 years

30 - 45 Lacs

noida, south goa, hyderabad

Hybrid

Job Title: GenAI Architect Experience Required: 10+ years in software architecture or machine learning roles, including at least 2+ years focused on Generative AI or Large Language Models (LLMs). Role Overview We are seeking a GenAI Architect to lead the design, development, and deployment of cutting-edge Generative AI solutions. This role requires deep expertise in LLMs, advanced AI architectures, and the ability to build scalable and production-ready systems leveraging the latest in AI technologies. Key Responsibilities Architect and implement GenAI solutions, including LLMs, RAG pipelines, embedding models, and vector search . Design and optimize prompt engineering strategies, fine-tuning approaches , and mitigate hallucinations in AI outputs. Develop robust AI workflows using Python, LangChain, Hugging Face Transformers , and vector databases such as Pinecone or Weaviate . Integrate and leverage GenAI APIs (OpenAI, Azure OpenAI, Anthropic, Google Vertex AI). Deploy AI applications in cloud-native environments (AWS, GCP, Azure) using Kubernetes and containerized architectures . Lead model evaluation, performance tuning, and ensure scalable architecture for enterprise use cases. Collaborate with business stakeholders to translate complex AI concepts into actionable solutions and value-driven outcomes. Required Skills & Qualifications 10+ years of experience in software architecture or machine learning, with 2+ years in GenAI/LLM-focused roles . Strong expertise in: LLMs and RAG pipelines Prompt engineering, fine-tuning, and hallucination mitigation LangChain, Hugging Face Transformers, Python Vector databases (Pinecone, Weaviate, etc.) Proficiency in deploying solutions on AWS/GCP/Azure with Kubernetes. Experience with model evaluation frameworks and industry best practices. Ability to communicate AI strategies and outcomes to non-technical stakeholders. Preferred Qualifications Hands-on experience with LLMOps tools (Weights & Biases, MLflow, Trulens, PromptLayer). Knowledge of domain-specific AI applications (real estate, finance, risk modeling). Exposure to multi-modal GenAI (text, image, document, voice). Contributions to open-source projects, research publications, or patents in AI/ML.

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9.0 - 14.0 years

30 - 45 Lacs

pune, bengaluru, mumbai (all areas)

Hybrid

Job Title: GenAI Architect Experience Required: 10+ years in software architecture or machine learning roles, including at least 2+ years focused on Generative AI or Large Language Models (LLMs). Role Overview We are seeking a GenAI Architect to lead the design, development, and deployment of cutting-edge Generative AI solutions. This role requires deep expertise in LLMs, advanced AI architectures, and the ability to build scalable and production-ready systems leveraging the latest in AI technologies. Key Responsibilities Architect and implement GenAI solutions, including LLMs, RAG pipelines, embedding models, and vector search . Design and optimize prompt engineering strategies, fine-tuning approaches , and mitigate hallucinations in AI outputs. Develop robust AI workflows using Python, LangChain, Hugging Face Transformers , and vector databases such as Pinecone or Weaviate . Integrate and leverage GenAI APIs (OpenAI, Azure OpenAI, Anthropic, Google Vertex AI). Deploy AI applications in cloud-native environments (AWS, GCP, Azure) using Kubernetes and containerized architectures . Lead model evaluation, performance tuning, and ensure scalable architecture for enterprise use cases. Collaborate with business stakeholders to translate complex AI concepts into actionable solutions and value-driven outcomes. Required Skills & Qualifications 10+ years of experience in software architecture or machine learning, with 2+ years in GenAI/LLM-focused roles . Strong expertise in: LLMs and RAG pipelines Prompt engineering, fine-tuning, and hallucination mitigation LangChain, Hugging Face Transformers, Python Vector databases (Pinecone, Weaviate, etc.) Proficiency in deploying solutions on AWS/GCP/Azure with Kubernetes. Experience with model evaluation frameworks and industry best practices. Ability to communicate AI strategies and outcomes to non-technical stakeholders. Preferred Qualifications Hands-on experience with LLMOps tools (Weights & Biases, MLflow, Trulens, PromptLayer). Knowledge of domain-specific AI applications (real estate, finance, risk modeling). Exposure to multi-modal GenAI (text, image, document, voice). Contributions to open-source projects, research publications, or patents in AI/ML.

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