Senior Staff Solution Architect

AI Platform & Solution Architecture

• Design and build scalable, multi-tenant AI platforms on AWS supporting diverse ML and GenAI use cases.

• Define reference architectures, reusable components, and best practices for AI/ML systems.

• Architect end-to-end AI pipelines spanning data ingestion, feature engineering, model training, deployment, and monitoring.

• Ensure platform readiness for SaaS environments, including tenant isolation, scalability, and cost optimization.

Machine Learning & Deep Learning Engineering

• Lead development of machine learning and deep learning models for production-grade applications.

• Establish standards for ML engineering, including data preprocessing, feature stores, training pipelines, and inference services.

• Optimize model performance, scalability, and latency for real-time and batch inference systems.

Generative AI & Agentic AI Systems

• Design and implement Generative AI solutions using AWS Bedrock and foundation models.

• Build RAG (Retrieval-Augmented Generation) pipelines with vector databases and embeddings.

• Develop agentic AI systems, including autonomous agents, orchestration frameworks, and tool integration.

• Apply advanced techniques such as prompt engineering, fine-tuning, evaluation frameworks, and guardrails.

AWS Cloud Architecture

• Architect and implement solutions using:

• AWS SageMaker (training, pipelines, deployment)

• AWS Bedrock (GenAI services)

• Compute: Lambda, ECS, EKS

• Orchestration: Step Functions

• Storage/Data: S3, Redshift, DynamoDB, Glue

• Build highly available, fault-tolerant, and cost-optimized cloud-native systems.

• Implement Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation.

MLOps & Model Lifecycle Management

• Establish and scale MLOps frameworks for continuous integration and deployment of ML models.

• Implement MLflow for experiment tracking, model registry, and lifecycle management.

• Build automated CI/CD pipelines for ML workflows.

• Enable model monitoring, drift detection, retraining pipelines, and explainability.

Data Architecture & Engineering

• Design scalable data architectures to support AI/ML workloads (batch and streaming).

• Implement data pipelines, feature stores, and data governance frameworks.

• Ensure data quality, lineage, and accessibility across teams.

Security, Compliance & Governance

• Architect AI systems with security-first principles, including encryption, IAM, and network controls.

• Ensure compliance with enterprise and regulatory standards (e.g., HIPAA, GDPR, SOC2 as applicable).

• Implement responsible AI practices, including bias detection, auditability, and explainability.

• Establish governance for data, models, and AI services.

SaaS Platform Engineering

• Design and implement AI-powered SaaS platforms, supporting:

  • Multi-tenancy and tenant isolation

  • API-driven architectures

  • Usage metering and cost attribution

• Enable platform extensibility and integration with enterprise systems.

Leadership & Collaboration

• Provide technical leadership and mentorship across AI, ML, and cloud engineering teams.

• Collaborate with product, engineering, and business stakeholders to translate requirements into scalable solutions.

• Lead architecture reviews, design discussions, and technical decision-making.

  • Drive innovation and adoption of emerging AI paradigms.