Cloud Engineer
WHR Global Consulting
Position: Senior Cloud & AI Engineer
Reports to: Data Manager
Department: Information Systems Technology
Job Summary
The Senior Developer will be responsible for designing, building, and leading the development of applications that power Data Science, AI, and Machine Learning initiatives. S/he excels in cloud-native development (AWS and/or Databricks), application integration, and building scalable data-driven systems. S/he will collaborate closely with the Data Team to turn analytics and AI concepts into production-grade solutions.
Essential Duties and Responsibilities
• Application Development & Architecture
o Architect, design, and build cloud-native applications in AWS (Lambda, API Gateway, ECS/EKS, Step Functions, etc) or Databricks (Workflows, Delta Live Tables, MLflow, Apps, etc).
o Build high-performance APIs, microservices, and automation workflows to support data science, ML model operations, and analytics applications.
o Lead end-to-end solutions architecture for data-driven products, from concept to production deployment.
o Ensure applications follow best practices for scalability, reliability, and security.
• Integration & Cloud Engineering
o Integrate applications with internal and external systems using APIs, event-driven design, messaging systems, or serverless services.
o Implement CI/CD pipelines and DevOps practices for smooth deployment and monitoring.
o Work closely with the data team to build services that integrate efficiently with data lakes, data warehouses, and ML pipelines.
• Data Science & ML Enablement
o Build interfaces, services, or tools that enable machine learning model training, deployment, and monitoring.
o Operationalize ML models (MLOps) using appropriate cloud or Databricks components.
o Collaborate with Data Scientists to transform prototypes into robust, scalable applications.
• Leadership & Collaboration
o Mentor fellow developers and provide technical guidance across teams.
o Contribute to architectural decisions and technical strategy.
o Work cross-functionally with other IT teams to understand requirements and translate them into technical solutions.
Performance Indicators
• Application Efficiency and Reliability
o Stability & Error Rates
o Feature Delivery Velocity
o Code Quality & Maintainability
• Integration Quality and System Interoperability
o Successful Integration to Internal and External Systems
o Performance and Latency
o Error Handling & Retry
• Cloud Architecture & Optimization
o Cloud Resource and Cost Efficiency
o Scalable Application Design
o CI/CD Pipeline Health
• ML / AI Application Enablement
o ML Model Deployment Efficiency
o MLOps Pipeline Reliability
o Collaboration with IT Teams
• Security, Compliance & Governance
o Secure Coding Practices
o Identity & Access Integration Quality
o Auditability & Logging Completeness
• Operational Excellence
o Incident Response & Mean Time to Resolution
o Uptime & Service Availability
o Automation Adoption
• Collaboration & Communication
• Innovation and Continuous Improvement
• Learning & Development
Qualifications
• Degree in Computer Science, Engineering, or related field; equivalent experience welcomed.
• 5+ years of hands-on software development experience, with strong proficiency in languages such as Python, Java, or Scala.
• Experience leading, implementing or supporting MLOps or DataOps frameworks.
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Experience working with distributed systems and big data technologies (Spark, Delta Lake, Kafka).
• Prior leadership experience (technical lead, project lead, or mentoring roles).
• Proven experience building production applications in AWS (Lambda, API Gateway, ECS/EKS, S3, RDS, DynamoDB, Step Functions, etc) or Databricks (Delta Live Tables, MLflow, Workflows, Apps, etc).
• Strong background in application and system integration (APIs, event streaming, webhooks, queues).
• Experience with CI/CD tools (GitHub Actions, etc.).
• Familiarity with ML lifecycle concepts (feature engineering, deployment, monitoring) or experience supporting data science teams.
• Strong understanding of modern software architecture: microservices, serverless, event-driven systems.
• Excellent problem-solving skills and ability to work on complex technical challenges.
• Strong communication skills and ability to collaborate with diverse teams.
• Ownership mindset; proactive in identifying improvements and driving solutions.
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