Boston Scientific Senior AI/ML Engineer 

Costa Rica, Heredia 

408543591

Hybrid Roles:

Job Overview:

In this role, you will collaborate with architects, platform engineers, data engineers, data scientists and product managers to deliver robust AI-powered products. Your primary responsibility will be to build, integrate, and deploy backend services, APIs, and frontend components that utilize AI models, ensuring seamless interaction between cloud infrastructure and application layers.

As a technical leader, you will guide the team in adopting best practices for full-stack development and AI integration while mentoring other engineers.

Key Responsibilities:

• Full-Stack AI Product Development: Lead the development of end-to-end AI-powered applications, focusing on integrating AI/ML models as services. This includes building both backend APIs and frontend components to deliver scalable, production-ready solutions.
• Agentic Workflow Automation: Architect and implement agentic workflows using Generative AI to automate business processes and drive operational efficiency.
• Backend & API Development: Design and develop robust backend services and APIs that seamlessly integrate AI models developed by the data science team, ensuring efficient inference and data flow between AI services and application components.
• Frontend Integration: Build or contribute to frontend components that interact with AI services, ensuring a cohesive user experience when embedding AI-driven features into existing or new applications.
• Cloud & Infrastructure Integration: Work closely with platform and cloud engineers to deploy AI-based applications on AWS, Azure, and Snowflake, ensuring scalability, security, and performance of the deployed solutions.
• Cross-Functional Collaboration: Collaborate with data scientists, data engineers, and other stakeholders to understand AI model requirements and ensure their integration into applications aligns with business objectives.
• Mentorship & Leadership: Mentor and guide junior engineers, setting best practices for software engineering, cloud integration, and AI service deployment.
• CI/CD & DevOps: Drive best practices for CI/CD pipelines and DevOps processes, ensuring smooth deployment and scaling of applications across cloud environments.

Required Skills and Experience:

• Full-Stack Development: Expertise in full-stack development, with proficiency in building backend services (RESTful APIs, microservices) and frontend components for data intensive applications. Strong knowledge of Python , JavaScript , and SQL is required.
• Cloud Services & Integration (Priority on Azure):
  • Azure (Must Have):Strong expertise in Azure cloud services, including AI Search, Document Intelligence, Assistant API, Promptflow, App Services, CosmosDB, MySQL, Blob Storage, and AI Studio.
  • AWS (Nice to Have): Familiarity with AWS services like Bedrock, Comprehend, SageMaker (for model inference), S3, ElasticSearch, and EKS.
  • Snowflake (Nice to Have): Experience withSnowpark Container ServicesandSnowflake Native Appsis an added advantage.
• AI/ML Integration: Experience in integrating AI/ML models (as services) into applications, focusing on model inference and service orchestration rather than model development.
• API Development & Integration: Strong expertise in designing and deploying APIs that integrate AI models and manage the interactions between AI services, data pipelines, and application components.
• Version Control & CI/CD: Advanced knowledge of Git and experience with automated deployment pipelines using CI/CD tools. Familiarity with GitHub Copilot or similar tools for enhancing development productivity is a plus.
• Cloud-Native Architecture: Experience in deploying applications on cloud environments using containerization (Docker) and orchestration (Kubernetes, especially EKS on AWS).
• DevOps & Deployment: Solid experience with DevOps practices for AI-driven applications, including continuous integration, monitoring, and automated scaling.

Desirable Skills:

• Experience with microservices architecture and containerized application development.
• Familiarity with MLOps pipelines, particularly for managing model serving and monitoring.
• Strong understanding of cloud security best practices and AI governance.

Educational & Experience Requirements:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• Minimum of 5 years of experience in software engineering, with a focus on building and deploying data intensive full-stack applications integrated with AI/ML models.