Senior AI Application Developer

Summary

Beth Israel Lahey Health is a growing healthcare organization dedicated to making a difference in people’s lives. The Senior AI Application Developer is responsible for designing, developing, testing, and supporting modern software solutions that leverage AI and cloud technologies, while collaborating with cross-functional teams to deliver advanced AI-enabled applications and maintain legacy systems.

Responsibilities

• Leverages expertise in AI application development, cloud-native architectures, modern web frameworks, and secure development practices to deliver scalable, reliable, and secure enterprise solutions
• Designs, develops, and integrates AI-enabled applications, cloud-based APIs, microservices, automation workflows, and data services across hybrid and cloud-native environments (Azure, AWS and GCP)
• Leads the design and implementation of proof-of-concepts (POCs) to evaluate emerging AI models, cloud services, and automation technologies, providing recommendations that guide technology adoption and architecture decisions
• Collaborates with cross-functional teams including product, data engineering, cloud engineering, and clinical/operational stakeholders to design and deliver cloud native, AI-augmented application features aligned with business goals
• Participates in gathering, analyzing, refining, and validating technical and functional requirements, ensuring alignment between business objectives, technical feasibility, and AI responsible use principles
• Creates development estimates, plans, and schedules to support predictable delivery and meet project deadlines
• Troubleshoots and debugs modern web, cloud, and AI-enabled applications; ensure code quality; and maintain clear, up-to-date documentation for application, data, and cloud components
• Provides mentorship and technical guidance to junior developers, promoting strong engineering practices in AI agent integration, cloud services, microservice architecture, and application development
• Collaborates in code reviews and architecture discussions to ensure solutions are secure, scalable, and operationally reliable
• Provides operational and on-call support for critical applications, ensuring system reliability, monitoring, incident response, and continuous improvement across AI and cloud workloads

Skills

• Bachelor's degree in computer science, Information Technology, or related field required
• 5+ years of relevant professional experience in software engineering or related technical fields
• 3–5+ years of hands-on software development experience, including building cloud-native and AI-enabled applications
• 1+ years of technical leadership experience, such as mentoring developers, leading small project teams, or guiding architecture decisions
• Strong hands-on experience designing, building, and deploying applications in cloud environments (AWS, Azure, or GCP), including computing, storage, networking, identity, and security services
• Practical expertise integrating or developing AI/GenAI solutions, including use of cloud AI services, LLM-based capabilities, AI agents, or machine learning assisted features
• Advanced programming and systems skills, with experience building APIs, automation workflows, microservices, and high-performance application components
• Deep understanding of secure by design and cloud security best practices, including identity management, encryption, secrets management, and compliance considerations
• Operational leadership experience, including monitoring, observability, incident response, performance tuning, and reliability engineering for cloud and AI-enabled applications
• Strong familiarity with DevOps and platform engineering practices, such as Git workflows, CI/CD pipelines, infrastructure as code, automated testing, and container orchestration
• Experience working across hybrid Windows and Linux environments, including automated deployments, system troubleshooting, and cloud-integrated operations
• Knowledge with Microsoft Power Platform, Azure DevOps, and AWS Bedrock for building, integrating, and automating cloud-based and AI-enabled solutions
• Knowledge of containerization and orchestration technologies such as AWS ECS and Kubernetes
• Knowledge of deploying, monitoring, and operating AI applications in production environments

Qualifications

Must Haves

• Bachelor's degree in computer science, Information Technology, or related field required
• 5+ years of relevant professional experience in software engineering or related technical fields
• 3–5+ years of hands-on software development experience, including building cloud-native and AI-enabled applications
• 1+ years of technical leadership experience, such as mentoring developers, leading small project teams, or guiding architecture decisions
• Strong hands-on experience designing, building, and deploying applications in cloud environments (AWS, Azure, or GCP), including computing, storage, networking, identity, and security services
• Practical expertise integrating or developing AI/GenAI solutions, including use of cloud AI services, LLM-based capabilities, AI agents, or machine learning assisted features
• Advanced programming and systems skills, with experience building APIs, automation workflows, microservices, and high-performance application components
• Deep understanding of secure by design and cloud security best practices, including identity management, encryption, secrets management, and compliance considerations
• Operational leadership experience, including monitoring, observability, incident response, performance tuning, and reliability engineering for cloud and AI-enabled applications
• Strong familiarity with DevOps and platform engineering practices, such as Git workflows, CI/CD pipelines, infrastructure as code, automated testing, and container orchestration
• Experience working across hybrid Windows and Linux environments, including automated deployments, system troubleshooting, and cloud-integrated operations

Nice to Haves

• Knowledge with Microsoft Power Platform, Azure DevOps, and AWS Bedrock for building, integrating, and automating cloud-based and AI-enabled solutions
• Knowledge of containerization and orchestration technologies such as AWS ECS and Kubernetes
• Knowledge of deploying, monitoring, and operating AI applications in production environments