Senior AI Application Developer

Beth Israel Lahey Health

19h

About The Position

When you join the growing BILH team, you're not just taking a job, you’re 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, web, mobile, and distributed microservices technologies, as well as cloud platforms. This role focuses on building advanced AI-enabled applications, intelligent agents, automated data processing workflows, and maintaining or modernizing legacy applications built in Java and .NET. The role also requires providing effective peer support, mentorship, and collaboration across development teams to troubleshoot complex issues and promote best practices in AI application development.

Requirements

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.

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.