AI/ML Deployment Engineer
The AI/ML Deployment Engineer is responsible for designing, deploying, and scaling AI and machine learning powered SaaS applications in production environments. This role focuses on application architecture, deployment automation, and infrastructure design to ensure high performance, low latency, and reliable customer experiences. The engineer owns end-to-end deployment of AI systems, partnering closely with engineering, DevOps, IT, and product teams to deliver secure, scalable, and production ready AI solutions. and machine learning powered SaaS applications in production environments. This role focuses on application architecture, deployment automation, and infrastructure design to ensure high performance, low latency, and reliable customer experiences. The engineer owns end-to-end deployment of AI systems, partnering closely with engineering, DevOps, IT, and product teams to deliver secure, scalable, and production ready AI solutions. and machine learning powered SaaS applications in production environments. This role focuses on application architecture, deployment automation, and infrastructure design to ensure high performance, low latency, and reliable customer experiences. The engineer owns end-to-end deployment of AI systems, partnering closely with engineering, DevOps, IT, and product teams to deliver secure, scalable, and production ready AI solutions.
Responsibilities
- Design, architect, and deploy AI/ML-powered SaaS web application consisting of and interacting with multiple backend services
- Take end-to-end ownership of deployment projects using structured and unstructured data types, including time series data sources.
- Experience architecting and deploying external facing web applications
- Ensure high performance, low latency, and reliability for external customer interactions and data processing workflows.
- Collaborate with cross-functional teams (IT, DevOps, and engineering) to define technical requirements and deliver scalable solutions.
- Optimize system architecture for speed, scalability, and security, balancing user experience and backend efficiency.
- Collaborate with cross-functional teams of engineers, product managers, and researchers to deliver high-quality products and services.
- Create, implement, and debug infrastructure and pipelines to automate the deployment process.
- Design systems using scaling strategies (scale in vs. Scale in vs. scale out) and proven architectural patterns to support growth and high availability
- Apply security best practices to protect SaaS platforms and customer data throughout the deployment.
Qualifications:
- Bachelor’s in Computer Science or related field required, Master’s degree preferred
- Equivalent combination of experience and education which clearly indicates the ability to perform the essential functions of the position may substitute on a year for year basis
- 4+ years of experience in AI/ML engineering, with a focus on software product deployment (SaaS preferred). required
- Experience with setting up staging environments required
- Proficient with RESTful APIs, microservices, and serverless architectures. required
- Proficient with infrastructure, Deployment and orchestration tools such as Terraform, Azure DevOps, Azure Container Apps, Azure Functions, Dockers, Azure pipelines, temporal, etc. required
- Experience with Kubernetes is a plus
- Proven track record delivering production-grade AI/ML models, including scalable data pipelines and monitoring systems.
- Demonstrated ability to architect systems for fast response times and high availability.
- Excellent problem-solving, collaborative, communication, and teamwork skills. required
- Able to use Microsoft Office Applications
Additional Requirements:
- 10% travel may be required