Job Summary
The Solution Architect is responsible for defining and governing the end-to-end architecture for a Application development, AI-powered personalization solution . This role leads technical solution design across data, ML, GenAI, cloud infrastructure, and serving layers, ensuring that the proposed architecture integrates with the current ranking ecosystem while meeting standards for scalability, security, and maintainability.
Key Responsibilities
- 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.
Required Skills
Core AI & Cloud Architecture
- 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.
Application Development & Integration
- 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.
Data Engineering & DevOps (MLOps)
- 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.
Leadership & Communication
- 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.
Preferred Qualifications
- 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.
Education
A Bachelor's/Master's degree in
Computer Science, Information Technology, or Software Engineering 
