Job Description

Summary:

The Data Scientist AI Product Design and Architecture Lead is a pivotal role that combines AI/ML expertise, product design, and cloud-native architecture to build innovative, scalable, and user-centric AI solutions. The ideal candidate must have hands-on experience in AI/ML development and a strong background in architecting large-scale AI solutions using AWS cloud services .

This role requires expertise in Agentic Frameworks, LangChain, Retrieval-Augmented Generation (RAG), and Generative AI (GenAI) to drive the next generation of AI-powered products. The candidate will lead cross-functional teams, ensuring seamless AI integration into cloud-based microservices architectures while prioritizing usability, scalability, and performance .

Key Responsibilities:

1. AI Product Design & Architecture:

• Lead the design and implementation of AI-driven product features with a user-first approach .
• Architect cloud-native AI solutions leveraging AWS services such as SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, and Step Functions .
• Utilize Agentic Frameworks to build AI-powered automation and intelligent decision-making workflows .
• Implement LangChain and RAG techniques to enhance AI model knowledge retrieval and contextual reasoning.
• Define and maintain scalable, secure, and cost-effective AI architectures aligned with business goals.

2. AWS Cloud-Native AI & DevOps:

• Design and deploy microservices-based AI applications on AWS with a focus on scalability, performance, and cost-efficiency .
• Implement CI/CD pipelines for AI/ML applications using AWS CodePipeline, CodeBuild, and CodeDeploy .
• Ensure infrastructure as code (IaC) with Terraform or AWS CloudFormation for automated provisioning.
• Monitor and optimize AI application performance using AWS CloudWatch, X-Ray, and Prometheus .
• Ensure security and compliance best practices in AI model deployment and data handling.

3. User-Centric AI Experience & Innovation:

• Champion human-AI interaction principles to ensure AI-driven features enhance usability and engagement.
• Leverage user research and behavioral analytics to design AI interfaces that are intuitive and accessible.
• Continuously iterate on AI-driven experiences based on user feedback, A/B testing, and performance analytics .

4. Cross-Functional Collaboration:

• Partner with product managers, engineers, and data scientists to drive AI innovation.
• Effectively communicate technical trade-offs and AI design decisions to non-technical stakeholders.
• Promote collaboration between AI, UX, and DevOps teams to ensure seamless AI product development.

5. AI Thought Leadership & Strategy:

• Stay ahead of AI and cloud computing advancements , incorporating emerging GenAI and AWS AI services into the product roadmap.
• Mentor and guide junior engineers, fostering AI/ML best practices across teams.
• Represent the organization in industry discussions, AI communities, and conferences .

Qualifications & Skills:

Required:

• 7+ years of experience in AI/ML development, cloud architecture, and AI product design.
• Expertise in Agentic Frameworks, LangChain, Retrieval-Augmented Generation (RAG), and Generative AI (GenAI) .
• Strong hands-on experience with AWS AI/ML services , including SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, Step Functions, and S3 .
• Experience architecting cloud-native AI solutions with AWS microservices, Kubernetes (EKS), and serverless computing .
• Proficiency in AWS DevOps tools , including CodePipeline, CodeBuild, CodeDeploy, Terraform, and CloudFormation .
• Strong background in microservices architecture, event-driven design, and scalable AI/ML pipelines .
• Experience with data visualization, AI-powered storytelling, and explainable AI (XAI) .
• Excellent problem-solving, communication, and cross-functional leadership skills .

Preferred:

• AWS certifications (AWS Certified Solutions Architect, AWS Certified DevOps Engineer, or AWS AI/ML certifications).
• Experience in building autonomous AI agents and knowledge-driven AI systems.
• Prior experience in optimizing AI model inference and deployment at scale

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