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
