About the role
- Define the target-state architecture for building, integrating AI-based application development.
- Hands on experience on Topic Ranking System
- Ensure alignment between the proposed personalization approach and current infrastructure investments, including the existing ranking model and serving environment.
- Lead architecture governance, technical decision-making, and solution design reviews throughout the engagement.
- Design system integration patterns across Amazon Bedrock, Amazon SageMaker, data storage, feature pipelines, and the serving layer.
- Assess and document current-state data flows, model architecture, training pipelines, and serving dependencies.
- Establish secure and compliant cloud architecture patterns, including encryption, protected service access, and production-grade deployment design.
- Partner with business and technical stakeholders to align architecture choices with program goals, POC outcomes, and future production rollout.
- Provide technical oversight during model evaluation, platform integration, and transition planning.
- Cloud-Native AI Design: Expert-level proficiency in designing scalable, cloud-native applications and AI/ML solutions specifically on AWS.
- AWS Service Deep-Dive: Hands-on mastery of Amazon SageMaker and Amazon Bedrock, paired with foundational infrastructure like S3, CloudWatch, and secure IAM/VPC integration patterns.
- Advanced ML Engineering: Deep understanding of ML architecture, including multi-task learning, model distillation, and high-performance feature-serving patterns.
- Full-Stack Engineering: Strong experience in modern application development (e.g., Python, Go, or Java) to build the APIs and microservices that consume AI outputs.
- Production Serving: Expertise in low-latency integration patterns and high-concurrency serving environments, utilizing NoSQL systems (DynamoDB, Redis) for real-time state management.
- System Design: Proven ability to build end-to-end recommendation, ranking, or personalization systems that are seamlessly integrated into consumer-facing applications.
- Distributed Data Systems: Proficiency in data platforms and distributed processing (PySpark, Hive) and managing large-scale data lakes to fuel the ML pipeline.
- DevOps & CI/CD: Experience implementing CI/CD pipelines for both code and models (MLOps), ensuring automated testing, versioning (Git), and zero-downtime deployments.
- Infrastructure as Code (IaC): Mastery of automating environment provisioning using tools like Terraform, AWS CloudFormation, or CDK to ensure reproducible AI environments.
- Observability: Ability to design comprehensive monitoring and alerting frameworks for application health, model drift, and system performance.
- Cross-Functional Leadership: Excellent stakeholder communication skills with the ability to translate complex AI/DevOps concepts into business value.
- Architectural Governance: Strong skills in creating technical documentation, RFCs, and system diagrams to align engineering and product teams.
- Experience modernizing legacy or existing applications, ML platforms and GenAI capabilities.
- Familiarity with architecture review frameworks and cloud best practices for security, reliability, and operational excellence.
- Experience supporting POCs that transition into production programs.
- Prior work in consumer platforms, digital commerce, food delivery, or high-scale personalization environments.
- Ability to translate business KPIs into technical architecture decisions and roadmap recommendations.