Senior Cloud & AI Engineer

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

oArchitect, 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).

oBuild high-performance APIs, microservices, and automation workflows to support data science, ML model operations, and analytics applications.

oLead end-to-end solutions architecture for data-driven products, from concept to production deployment.

oEnsure applications follow best practices for scalability, reliability, and security.

• Integration & Cloud Engineering

oIntegrate applications with internal and external systems using APIs, event-driven design, messaging systems, or serverless services.

oImplement CI/CD pipelines and DevOps practices for smooth deployment and monitoring.

oWork closely with the data team to build services that integrate efficiently with data lakes, data warehouses, and ML pipelines.

• Data Science & ML Enablement

oBuild interfaces, services, or tools that enable machine learning model training, deployment, and monitoring.

oOperationalize ML models (MLOps) using appropriate cloud or Databricks components.

oCollaborate with Data Scientists to transform prototypes into robust, scalable applications.

• Leadership & Collaboration

oMentor fellow developers and provide technical guidance across teams.

oContribute to architectural decisions and technical strategy.

oWork cross-functionally with other IT teams to understand requirements and translate them into technical solutions.

Performance Indicators

• Application Efficiency and Reliability

oStability & Error Rates

oFeature Delivery Velocity

oCode Quality & Maintainability

• Integration Quality and System Interoperability

oSuccessful Integration to Internal and External Systems

oPerformance and Latency

oError Handling & Retry

• Cloud Architecture & Optimization

oCloud Resource and Cost Efficiency

oScalable Application Design

oCI/CD Pipeline Health

• ML / AI Application Enablement

oML Model Deployment Efficiency

oMLOps Pipeline Reliability

oCollaboration with IT Teams

• Security, Compliance & Governance

oSecure Coding Practices

oIdentity & Access Integration Quality

oAuditability & Logging Completeness

• Operational Excellence

oIncident Response & Mean Time to Resolution

oUptime & Service Availability

oAutomation Adoption

• Collaboration & Communication

• Innovation and Continuous Improvement

• Learning & Development

Qualifications

•Degree in Computer Science, Engineering, or related field; equivalent experience welcomed.

•5-8 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.