Enterprise Architect

 

Bachelor’s degree in Computer Science or related field required; Master’s degree preferred.
12+ years of progressive hands‑on experience in application development, analysis, engineering, solution architecture, and technical leadership.
Minimum 5 years of experience as a solution architect working with AWS or Azure.
Experience with Architecture principles and the TOGAF framework is a plus.
AWS Professional Certification preferred; AWS or Azure Architecture Associate Certification required.
CISSP or equivalent security certification is a plus.
Technical Expertise
Enterprise Data & Cloud Architecture
Expertise managing Enterprise Data Platforms including Data Lakes, Data Warehouses, and Data Marts.
Experience with real-time and near real-time data streaming platforms.
Expertise with relational, semi-structured, and unstructured databases.
Strong proficiency with Python or Java.
Experience designing large-scale APIs, microservices, and distributed streaming-based solutions.
Skilled in supporting and managing large, complex, and geographically distributed cloud environments.
Strong background in risk assessment, control design, gap remediation, and impact analysis.
MLOps Expertise
Extensive experience with MLOps frameworks and enterprise ML lifecycle automation, including:
ML Lifecycle Architecture & Automation
Designing and implementing end‑to‑end MLOps pipelines: data ingestion, feature engineering, model training, tuning, evaluation, versioning, CI/CD for ML, approvals, and automated deployment.
Establishing model governance, including lineage, auditability, explainability, data validation, and responsible AI controls.
Model Deployment, Serving & Monitoring
Designing microservice-based ML inference architectures using EKS/ECS, Lambda, Step Functions, and event-driven patterns.
Implementing advanced model monitoring:
drift detection
data quality checks
outlier detection
performance degradation alerts
CloudWatch / OpenTelemetry observability pipelines
CI/CD for ML (MLOps)
Building automated ML pipelines using CodePipeline, Bitbucket Pipelines, GitHub Actions, Jenkins, etc., integrated with container registries and SageMaker.
Defining enterprise patterns for ML environment standardization, reproducibility, and secure deployment.
Microservice Orchestration & Cloud-Native Engineering
Experience with orchestrating microservices using AWS ECS, EKS, Fargate, Lambda, EventBridge, App Mesh, and Step Functions.
Implementing service mesh patterns for service discovery, traffic routing, observability, and zero-trust communication.
Designing event-driven architectures leveraging Kinesis, SNS/SQS, and Lambda.
Experience building highly available, resilient, fault-tolerant cloud architectures.
AWS & Cloud Infrastructure Expertise
Proficiency with RDS PostgreSQL, Aurora, DynamoDB, and other AWS data services.