About the Role :

We are looking for an AI Deployment Engineer to join our internal AI Deployment team and build the technical infrastructure that enables AI solutions to move from prototype to reliable, production-scale deployments.

While the AI Deployment Strategist focuses on scoping, designing, and building AI agents and solutions, this role will own the infrastructure and engineering backbone that supports those deployments. You will work across data pipelines, APIs, integrations, cloud infrastructure, monitoring, and system reliability to ensure AI solutions integrate seamlessly with existing business systems and operate effectively at scale.

This is a hands-on engineering role for someone who enjoys solving complex integration and data challenges and is comfortable taking systems from development and prototyping through to production.

Key Responsibilities :

- Own and develop the data infrastructure supporting AI deployments, including data pipelines, storage, processing, and data-serving layers.

- Design, build, and maintain production-grade data pipelines using Python and SQL.

- Integrate AI solutions with existing enterprise systems, including CRMs, ERPs, SaaS applications, Microsoft 365, Microsoft Copilot, and internal business systems.

- Develop and maintain REST APIs, webhooks, middleware, and system integrations that enable AI agents to interact with business applications.

- Implement authentication and authorization mechanisms such as OAuth and secure API integrations.

- Take AI prototypes developed by the deployment team and transition them into production-ready, scalable, and reliable systems.

- Design and implement event-driven architectures for asynchronous and real-time AI workflows.

- Build and manage workflow orchestration using tools such as Apache Airflow, Prefect, or Dagster.

- Design and maintain data models, schemas, databases, and storage architectures required for current and future AI deployments.

- Implement monitoring, logging, alerting, and observability across data pipelines, integrations, and AI infrastructure.

- Troubleshoot and resolve production issues involving integration failures, data inconsistencies, pipeline failures, API issues, and system performance.

- Work closely with AI Deployment Strategists, engineering teams, and business stakeholders to understand requirements and build scalable technical solutions.

- Ensure deployed systems meet appropriate standards for reliability, security, scalability, maintainability, and performance.

- Continuously improve the infrastructure and engineering practices supporting the organization's AI deployment ecosystem.

Required Technical Skills :

- Python : 3+ years of hands-on experience developing production-grade applications, data pipelines, or integrations.

- SQL : 3+ years of experience working with databases, complex queries, data transformation, and pipeline development.

- Apache Flink : 3+ years of experience with real-time data processing and streaming workloads.

- Strong experience with REST APIs, webhooks, OAuth, and middleware development.

- Understanding of event-driven architecture and asynchronous system design.

- Hands-on experience with at least one major cloud platform : AWS, GCP, or Azure.

- Experience with Docker and Kubernetes for containerization and deployment.

- Experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.

- Working knowledge of Microsoft 365 and Microsoft Copilot and their integration within enterprise environments.

- Strong understanding of data modelling, schemas, storage systems, and data-serving architectures.

Good to Have / Desirable Skills :

- Experience working with vector databases and embedding pipelines.

- Exposure to real-time streaming technologies such as Kafka and Apache Flink.

- Experience with RPA platforms such as UiPath or Microsoft Power Automate.

- Experience with dbt for data transformation and analytics engineering.

- Exposure to AI-assisted development tools such as Claude Code, Claude Cowork, or Claude Skills.

- Experience building infrastructure for AI agents, LLM-based applications, or AI automation solutions.

- Understanding of embedding generation, vector search, retrieval pipelines, and AI data architectures.

- Experience integrating enterprise AI solutions with multiple third-party SaaS platforms.

Experience & Qualifications :

- 3 - 5 years of professional experience in Software Engineering, Data Engineering, Platform Engineering, Integration Engineering, or a related technical discipline.

- Strong hands-on experience with Python, SQL, and Apache Flink.

- Proven experience building and maintaining production-grade data pipelines and system integrations.

- Experience taking technical prototypes or proof-of-concepts into production environments.

- Strong troubleshooting and problem-solving capabilities.

- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field is preferred.

What Success Looks Like :

In this role, you will be successful when you can :

- Reliably move AI solutions from prototype to production.

- Build scalable data pipelines and infrastructure that support AI deployments.

- Seamlessly integrate AI agents with enterprise applications and business systems.

- Ensure deployed solutions are observable, reliable, and production-ready.

- Quickly identify and resolve data, integration, and infrastructure issues.

- Build a robust technical foundation that enables the AI Deployment team to scale its impact across the organization.