New opportunity

Senior AI Application Developer

Beth Israel Lahey Health · Charlestown, United States

About this role

The Senior AI Application Developer designs, develops, tests, and supports AI-enabled software for Beth Israel Lahey Health. The role spans web and mobile applications, distributed microservices, cloud platforms, intelligent agents, and automated data processing workflows. It also includes maintaining or modernizing legacy Java and .NET applications.

Responsibilities

  • Build and integrate AI-enabled applications, cloud APIs, microservices, automation workflows, and data services across hybrid and cloud-native environments using Azure, AWS, and GCP.
  • Lead proofs of concept evaluating emerging AI models, cloud services, and automation technologies; recommend approaches that inform architecture and technology adoption.
  • Work with product, data engineering, cloud engineering, and clinical or operational stakeholders to define requirements and deliver features aligned with business goals, technical feasibility, and responsible AI use.
  • Prepare development estimates and plans; troubleshoot applications, maintain code quality and documentation, and participate in code reviews and architecture discussions.
  • Mentor junior developers and provide operational and on-call support, including monitoring, incident response, and reliability improvements for critical applications.

Required qualifications

  • Bachelor’s degree in computer science, information technology, or a related field; 5+ years of relevant professional experience in software engineering or a related technical field; 3–5+ years of hands-on software development, including cloud-native and AI-enabled applications; and 1+ years of technical leadership.
  • Experience building and deploying applications on AWS, Azure, or GCP; integrating AI or GenAI solutions such as cloud AI services, LLM capabilities, agents, or machine-learning-assisted features; and developing APIs, workflows, microservices, and high-performance components.
  • Knowledge of cloud security, identity, encryption, secrets management, monitoring, observability, incident response, performance tuning, DevOps, Git, CI/CD, infrastructure as code, automated testing, and container orchestration. Experience with hybrid Windows and Linux environments is required.

Preferred

Knowledge of Microsoft Power Platform, Azure DevOps, AWS Bedrock, AWS ECS, Kubernetes, and operating AI applications in production. This is a full-time, 40-hour-per-week day-shift position in Charlestown, Massachusetts. The annual base salary range is $118,000–$160,000 USD. Influenza vaccination is a condition of employment.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI application developmentGenerative AILarge language modelsAI agentsMachine learningAWSAzureGoogle Cloud PlatformCloud-native architectureAPI developmentMicroservicesAutomation workflowsJava.NETCloud securityIdentity managementEncryptionSecrets managementMonitoringObservabilityIncident responsePerformance tuningReliability engineeringDevOpsGitCI/CDInfrastructure as codeAutomated testingContainer orchestrationWindows administration under hybrid environments as experience? no

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A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Deliver integrated AI applications

    The role designs and builds AI-enabled applications, agents, APIs, microservices, automation workflows, and data services across hybrid and cloud-native environments.

  • Turn proofs of concept into architecture recommendations

    The developer leads evaluations of emerging AI models, cloud services, and automation technologies, then recommends approaches to guide adoption and architecture decisions.

  • Align features with stakeholder needs

    Work with product, data, cloud engineering, and clinical or operational stakeholders to refine requirements against business goals, technical feasibility, and responsible AI use.

  • Own secure, reliable operation

    The posting calls for secure-by-design development, testing, observability, troubleshooting, and on-call incident support for critical AI and cloud applications.

  • Provide senior engineering guidance

    Prepare estimates and plans, contribute to reviews and architecture discussions, mentor junior developers, and maintain or modernize Java and .NET applications.

Likely challenges to think through

  • Balancing AI experimentation with production readiness

    Likely preparation challenge: a promising proof of concept may not meet reliability, security, or responsible-use needs. Prepare an example of evaluation criteria, limitations, and a recommendation that distinguishes a demonstration from a deployable approach.

  • Modernizing without disrupting existing systems

    Likely preparation challenge: integrating new AI and cloud services with legacy Java or .NET applications across hybrid Windows and Linux environments may require incremental change rather than replacement. Prepare to explain integration boundaries, testing, and rollback trade-offs.

  • Reconciling competing delivery constraints

    Likely preparation challenge: clinical or operational needs, technical feasibility, and deadlines may point to different scopes. Prepare an example of refining requirements with stakeholders, making an estimate, and explaining what you would defer or validate first.

03

Practice your answers

Technical practice

How would you integrate an LLM-based agent into an application while keeping its behavior controlled?

What to coverThe role includes AI-enabled applications, intelligent agents, and responsible AI use. Suggested answer focus: define the agent's permitted actions, validate inputs and outputs, handle failures, and explain how you would test behavior before release.

How would you design a proof of concept to compare AI models or cloud AI services?

What to coverThe posting asks this role to lead proofs of concept and recommend approaches for adoption. Suggested answer focus: agree on use-case criteria, compare quality, latency, cost, security, and operational fit, then document the evidence behind a recommendation.

How would you design APIs and microservices for an AI-augmented web or mobile feature?

What to coverThe role spans web and mobile applications, cloud APIs, and distributed microservices. Suggested answer focus: describe service boundaries, API contracts, failure handling, and the trade-off between independent scaling and added operational complexity.

How would you make an automated data-processing workflow reliable?

What to coverBuilding automation workflows and data services is a stated responsibility. Suggested answer focus: address input validation, retry and duplicate handling, error visibility, and how the workflow would recover from a partial failure.

How would you choose a deployment architecture for an application spanning cloud and hybrid environments?

What to coverThe posting names hybrid and cloud-native work, with experience on AWS, Azure, or GCP and across Windows and Linux. Suggested answer focus: compare compute, storage, networking, and deployment options against integration needs, security, reliability, and maintainability rather than assuming one platform is required.

How would you apply secure-by-design principles to an AI-enabled cloud application?

What to coverIdentity management, encryption, secrets management, and compliance considerations are explicitly listed. Suggested answer focus: explain access boundaries, protection of data and credentials, and security checks at service interfaces and during deployment.

What would you put in a delivery pipeline for cloud-native applications?

What to coverThe posting calls for familiarity with Git, CI/CD, infrastructure as code, automated testing, and container orchestration. Suggested answer focus: walk through review, tests, repeatable provisioning, deployment, and rollback, noting where controls protect production reliability.

How would you diagnose a performance or reliability issue in a production AI application?

What to coverMonitoring, observability, incident response, performance tuning, and on-call support are part of the role. Suggested answer focus: use evidence to isolate whether the issue lies in the application, AI dependency, or infrastructure; then discuss mitigation, verification, and a lasting improvement.

How would you approach modernizing a legacy Java or .NET application?

What to coverMaintaining or modernizing legacy Java and .NET applications is stated in the role summary. Suggested answer focus: assess dependencies and behavior, add tests around critical paths, and weigh incremental changes against replacement while preserving operational continuity.

How would you test an AI-enabled feature before and after deployment?

What to coverThe role includes testing AI-enabled software and supporting AI and cloud workloads in production. Suggested answer focus: distinguish conventional functional and integration tests from evaluation of variable AI outputs, and explain how monitoring would reveal regressions after release.

Behavioural practice

Tell me about a time you reconciled competing stakeholder requirements for a technical feature.

What to coverThe role collaborates with product, data engineering, cloud engineering, and clinical or operational stakeholders. Suggested answer structure: give the situation, your actions to clarify goals and feasibility, and the resulting decision or delivery outcome.

Tell me about a time you mentored a developer through a difficult engineering problem.

What to coverMentorship and technical guidance for junior developers are stated responsibilities. Suggested answer structure: describe the problem, how you coached rather than simply took over, and what changed as a result.

Tell me about a time you challenged or changed an architecture proposal in a review.

What to coverThe posting calls for participation in code reviews and architecture discussions about security, scale, and reliability. Suggested answer structure: explain the disagreement, the evidence and trade-offs you raised, and the decision and its outcome.

Tell me about a time an estimate or delivery plan had to change.

What to coverPreparing estimates, plans, and schedules is part of this role. Suggested answer structure: describe the initial assumptions, how you surfaced new information and revised the plan, and the effect on delivery or stakeholder expectations.

Tell me about a time you coordinated a response to a critical application incident.

What to coverThe role includes operational and on-call support, incident response, and continuous improvement. Suggested answer structure: identify the situation, your actions to coordinate mitigation and communication, and the recovery and follow-up result.

02

Sharpen your knowledge

Topics to prepare

  • Hybrid cloud and microservice architecture

    Suggested exercise: Sketch an AI-enabled application spanning cloud services and an existing Windows or Linux system; explain service boundaries, data flow, and failure points. Relevance: The posting calls for APIs, microservices, and data services across hybrid and cloud-native environments.

  • AI integration and responsible use

    Suggested exercise: Compare an LLM-based feature, an agent, and a machine-learning-assisted feature for a hypothetical operational request; identify limitations and questions to validate with stakeholders. Relevance: The role integrates AI/GenAI capabilities and aligns requirements with responsible AI use principles.

  • Secure-by-design cloud applications

    Suggested exercise: Review a hypothetical application design for identity, encryption, secrets management, and compliance considerations; explain what you would change and why. Relevance: The posting explicitly requires deep understanding of these cloud security practices.

  • Reliability of production AI applications

    Suggested exercise: Walk through how you would detect, diagnose, and prioritize a slowdown in a critical AI-enabled service, including what monitoring evidence you would need. Relevance: On-call support, observability, incident response, performance tuning, and reliability engineering are stated responsibilities.

  • Technical leadership and delivery decisions

    Suggested exercise: Outline how you would refine an ambiguous feature request with product, engineering, and clinical or operational stakeholders, then estimate the work and explain an architecture tradeoff to a junior developer. Relevance: The role includes requirements validation, planning, architecture discussions, and mentorship.

Skills to revise

  • Build an AI integration proof of concept

    Suggested exercise: Create a small proof of concept that calls an AI capability through an application API; test a failure case and write a brief recommendation covering feasibility and limitations. Relevance: The role leads POCs evaluating AI models and services and integrates AI-enabled application features.

  • Implement an API and automation workflow

    Suggested exercise: Build a small API that accepts a request, triggers a data-processing workflow, and returns a clear result or error; add automated tests. Relevance: The posting specifies APIs, automation workflows, data services, microservices, and testing.

  • Practice cloud deployment and delivery

    Suggested exercise: Deploy a small application using one of AWS, Azure, or GCP; document its compute, storage, networking, identity, and security choices, plus a Git-based CI/CD and infrastructure-as-code approach. Relevance: Cloud deployment and these platform and DevOps practices are explicit requirements; the posting does not require using all three clouds for one exercise.

  • Troubleshoot across Windows and Linux

    Suggested exercise: Rehearse diagnosing a failed automated deployment or application-to-cloud connection on each operating system, recording the checks, findings, and recovery steps. Relevance: The posting requires hybrid Windows and Linux experience, system troubleshooting, and cloud-integrated operations.

  • Review a legacy modernization change

    Suggested exercise: Take a small Java or .NET application example and outline an incremental change that exposes a service interface while preserving existing behavior; specify regression tests and rollback considerations. Relevance: Maintaining or modernizing legacy Java and .NET applications is part of the role. The example approach is a preparation suggestion, not a prescribed employer design.

  • Run an incident and code-review drill

    Suggested exercise: For a hypothetical AI-service failure, draft a short incident timeline and reliability follow-up; separately review a sample change for security, tests, documentation, and operational impact. Relevance: The role provides on-call support and participates in code reviews to keep applications secure and reliable.

04

Your preparation checklist

  • Map the stated requirements to evidence

    Prepare a one-page reference to relevant education and work examples. The posting specifies a related bachelor’s degree, 5+ years of relevant experience, 3–5+ years of hands-on development including cloud-native and AI-enabled applications, and 1+ years of technical leadership; do not claim experience you cannot substantiate.

  • Rehearse an AI application walkthrough

    If you have a relevant project, prepare a five-minute account of its user need, AI or agent integration, APIs or workflows, cloud deployment, tests, results, and your own decisions. These elements reflect the applications this role builds and supports.

  • Prepare a proof-of-concept recommendation

    Sketch how you would compare two plausible AI models or cloud services for a stated use case. Bring evaluation criteria, a small test plan, responsible-use considerations, trade-offs, and a recommendation; the role leads POCs that inform architecture decisions.

  • Practice a hybrid architecture exercise

    Draw a proposed AI-enabled service connected to an existing Java or .NET application across cloud and hybrid environments. Be ready to explain API boundaries, data flow, Windows and Linux deployment considerations, scaling, and a staged modernization approach. Treat the design as an exercise, not an existing BILH system.

  • Show how you would secure and ship the design

    Add a brief checklist covering identity, encryption, secrets, automated tests, Git and CI/CD, infrastructure as code, and container orchestration. The posting calls for secure development and familiarity with these delivery practices.

  • Prepare an operations example

    If available, select an incident or reliability improvement you worked on. Outline detection, observability, troubleshooting, response, performance or reliability changes, and documentation; the role includes monitoring and on-call support for critical applications.

  • Outline a leadership and delivery example

    Prepare a concise account of mentoring or an architecture decision, including how you refined requirements with stakeholders, estimated work, handled trade-offs, and used code review. The posting names technical leadership, cross-functional collaboration, planning, and peer support.

  • Bring targeted questions for the employer

    Ask which AI-enabled applications and legacy systems are priorities, how clinical or operational stakeholders validate requirements and responsible AI use, and what the on-call scope and production support expectations are. These are questions to clarify, not assumptions about BILH’s processes.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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