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, OpenSearch
  • Azure: Azure OpenAI, Azure ML, Azure Cognitive Services, Cognitive Search
  • Strong 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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