Architect

4 weeks ago

Pune, India Neurealm Full time

About the Role – AI Architect (Generative AI)We are seeking a visionary and technically strong AI Architect to lead the design, development, and deployment of Generative AI solutions across AWS and Azure environments. This is a critical role for shaping our GenAI strategy across global enterprise customers.Key Responsibilities:Architect and implement GenAI solutions on AWS (Bedrock, SageMaker) and Azure (Azure OpenAI, Azure ML).Design both agentic and non-agentic workflows using tools like LangChain , Semantic Kernel , or AWS Agent Framework .Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases (e.g., Amazon OpenSearch, Azure Cognitive Search).Build and manage prompt engineering strategies and prompt lifecycle.Evaluate and integrate leading foundation models (e.g., GPT , Claude , Titan , Phi-2 , Falcon , Mistral ).Implement chunking/indexing strategies for unstructured data to support RAG and vector-based retrieval.Ensure responsible AI practices, including governance, security, explainability, and compliance.Collaborate with data engineering and DevOps teams for pipeline integration, model lifecycle, and CI/CD automation.Develop reference architectures and best practices for reusable GenAI components.Stay up to date with AWS/Azure GenAI innovations and provide strategic guidance.Required Qualifications :8+ years of experience in software/data architecture , including 3+ years in AI/ML with hands-on Generative AI experience.Proven ability to design and deploy AI workflows on:AWS : Amazon Bedrock, SageMaker, Lambda, DynamoDB, OpenSearchAzure : Azure OpenAI, Azure ML, Azure Cognitive Services, Cognitive SearchStrong experience in RAG , prompt engineering , and vector database design .Familiar with AI agent orchestration frameworks (LangChain, Semantic Kernel, AWS Agent Framework).Solid understanding of cloud security , IAM/RBAC , and compliance in enterprise settings.Proficiency in Python and modern ML libraries/APIs across AWS and Azure ecosystems.Preferred Qualifications :Experience with LLMOps tools: model monitoring, logging, performance tracking.Understanding of fine-tuning , evaluation , and GenAI safety/risk management .Familiarity with serverless architecture , containerization (ECS, AKS), and CI/CD pipelines in AWS/Azure.Ability to convert business needs into scalable, measurable AI solutions .

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