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.