New opportunity

Manager, AI Software Development

Beth Israel Lahey Health · Charlestown, United States

About this role

Beth Israel Lahey Health seeks a manager to lead a portfolio of technology applications and a team of full-stack developers. The role advances enterprise AI capabilities by delivering safe, scalable AI-enabled software for clinical, operational, and administrative uses, alongside web, mobile, microservices, and cloud solutions.

Responsibilities

  • Translate business objectives into application and AI-enabled solutions. Lead architectural decisions and software development across hybrid-cloud, cloud-native, and AI technologies, balancing scalability, performance, robustness, and fault tolerance.
  • Lead and contribute hands-on to proofs of concept for emerging technologies, including applied AI, machine learning, and generative AI. Oversee development and production deployment of AI-enabled applications from concept through operationalization.
  • Conduct design and code reviews; guide design, testing, deployment, and post-production support. Address technical blockers involving infrastructure, data pipelines, security, model deployment, and application performance.
  • Partner with Architecture teams on reference architectures and LLM adoption patterns, including RAG, agent workflows, and secure prompt orchestration, across on-premise environments, AWS, Azure, and GCP. Work with DevOps, Infrastructure, and Security teams to improve automation, CI/CD, MLOps, and operational reliability.
  • Lead incident investigations and resolution, including AI- and model-related failures. Coach developers, support career development, and manage daily work, hiring, corrective action, and performance reviews.

Required qualifications

A bachelor's degree in computer science, Information Technology, or a related field; 8+ years of related work experience, including 4+ years of hands-on software development and 2+ years of leadership or management experience. The role requires knowledge of AI technologies, cloud architectures, secure computing, and operational frameworks, plus cross-functional leadership, complex problem-solving, and effective written and verbal English communication.

Preferred qualifications

Experience delivering AI, machine learning, and generative AI solutions; knowledge of AWS, Azure, GCP, and hybrid architectures; and demonstrated leadership of full-stack development, modern software development practices, CI/CD, and MLOps.

This is a full-time, 40-hour-per-week day-shift position in Charlestown, Massachusetts. The annual base salary range is $155,000.00–$185,000.00 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 software developmentGenerative AIMachine learningFull-stack developmentWeb developmentMobile developmentMicroservicesCloud architectureAWSAzureGCPLarge language modelsRAGAI agentsPrompt orchestrationModel integrationModel deploymentMLOpsCI/CDDevOpsData pipelinesSoftware architectureSecure data usageCode reviewTechnical leadershipPeople managementIncident response

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Get interview-ready for this role

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

  • Translate needs into portfolio decisions

    The posting assigns this manager a portfolio of applications across clinical, operational, and administrative uses. Prepare an example of turning competing business objectives into delivery priorities and explaining the trade-offs to stakeholders.

  • Move AI concepts into production

    The role includes hands-on proofs of concept and oversight of AI and machine-learning applications from design through deployment and support. Prepare to distinguish what a proof of concept validates from what production readiness requires.

  • Set safe, scalable architecture

    The manager leads architectural decisions and works with Architecture teams on reference patterns, including RAG, agent workflows, and secure prompt orchestration. Prepare a decision framework covering secure data use, scalability, performance, and fault tolerance.

  • Own engineering quality and recovery

    The posting calls for design and code reviews, testing and deployment guidance, removal of technical blockers, and leadership during incidents, including model-related failures. Prepare an example that connects preventive engineering decisions with diagnosis and root-cause resolution.

  • Lead developers and cross-functional delivery

    The role manages full-stack developers through coaching, daily direction, hiring, and performance reviews while partnering with DevOps, Infrastructure, and Security. Prepare examples of developing engineers and resolving dependencies without losing delivery accountability.

Likely challenges to think through

  • Experimentation versus production safeguards

    Likely challenge, not a confirmed employer problem: emerging AI approaches may be quick to demonstrate but harder to deploy safely and reliably. Prepare to explain how you would set proof-of-concept success criteria and decide whether to advance, revise, or stop an approach.

  • Consistency across heterogeneous environments

    Likely challenge, not a confirmed employer problem: reference patterns spanning on-premise systems, hybrid cloud, AWS, Azure, and GCP may conflict with application-specific performance or security needs. Prepare to discuss where you would standardize and where you would permit documented exceptions.

  • Incident response versus planned delivery

    Likely challenge, not a confirmed employer problem: leading investigations into application or model failures can compete with portfolio work and coaching time. Prepare an example of restoring service, coordinating teams, identifying root cause, and adjusting priorities afterward.

03

Practice your answers

Technical practice

How would you translate a business objective into an AI-enabled application?

What to coverThe role calls for aligning application and AI solutions with business needs across clinical, operational, and administrative domains. Suggested preparation: explain how you would define the intended outcome, assess whether AI is appropriate, and establish measures of usefulness, safety, and operational fit.

How would you choose an architecture for an application spanning on-premise and cloud environments?

What to coverThe posting calls for architectural decisions across hybrid-cloud and cloud-native technologies, with attention to robustness, scalability, performance, and fault tolerance. Suggested preparation: compare placement and integration options, explaining how security, dependencies, and failure modes affect the trade-offs.

How would you design a proof of concept for an emerging AI approach?

What to coverThe manager is expected to lead and contribute hands-on to proofs of concept for applied AI, machine learning, and GenAI. Suggested preparation: describe a bounded hypothesis, evaluation criteria, and a decision point for whether to develop the approach further.

When would you use RAG, an agent workflow, or secure prompt orchestration?

What to coverThe role partners with Architecture teams on these LLM adoption patterns. Suggested preparation: distinguish their purposes and discuss trade-offs involving grounding, workflow complexity, access to data, and control over outputs.

What would you look for in a design or code review of an AI-enabled application?

What to coverThe posting explicitly includes reviews covering AI engineering, model integration, and secure data usage patterns. Suggested preparation: explain how you would examine data handling, integration boundaries, testability, and compliance with agreed design standards.

How would you structure a full-stack application that uses distributed microservices and AI capabilities?

What to coverThe team develops full-stack solutions using web, mobile, microservices, cloud services, and AI. Suggested preparation: discuss service boundaries, interfaces, and how the application should handle latency or failure in an AI-dependent component.

How would you take a machine-learning or GenAI application from concept to production support?

What to coverThe manager guides design, testing, deployment, and post-production support through operationalization. Suggested preparation: describe how testing, release decisions, monitoring, and support responsibilities would change when application behavior depends on a model.

How would you improve CI/CD and MLOps across a mixed application portfolio?

What to coverThe role works with DevOps, Infrastructure, and Security to improve automation, CI/CD, MLOps, and operational reliability. Suggested preparation: identify repeatable checks and deployment controls, and explain how you would balance delivery speed with safe changes.

How would you diagnose a blocker involving data pipelines, model deployment, and application performance?

What to coverResolving escalated, cross-team blockers in these areas is an explicit duty. Suggested preparation: describe a data-driven way to isolate the failing layer, gather evidence across teams, and distinguish a model issue from an infrastructure or application issue.

How would you respond to an outage involving an AI or model-related failure?

What to coverThe posting assigns the manager responsibility for rapid restoration and root-cause resolution during incidents. Suggested preparation: separate immediate service recovery from investigation, then explain how findings would inform testing, monitoring, or architectural changes.

Behavioural practice

Tell me about a time you reconciled competing objectives for a technology portfolio.

What to coverThe role sets priorities and makes decisions with implications across departments. Suggested preparation: use situation, action, and result to show how you weighed business needs, technical constraints, and operational reliability.

Describe a time you resolved a technical disagreement across teams.

What to coverThe manager collaborates with Architecture, DevOps, Infrastructure, and Security and removes cross-team blockers. Suggested preparation: use situation, action, and result to explain how you surfaced the disagreement, reached a decision, and communicated it.

Tell me about a time you led people through a production incident.

What to coverIncident investigation, response, and root-cause resolution are stated responsibilities. Suggested preparation: use situation, action, and result to distinguish your coordination and communication from the technical fix and its follow-up.

Describe a time you coached a developer’s growth while delivering team commitments.

What to coverThe posting calls for regular 1:1s, coaching, career development, and building engineering capability. Suggested preparation: use situation, action, and result to show how you identified a development need, supported progress, and assessed the outcome.

Tell me about a difficult performance or staffing decision you managed.

What to coverThe role has direct responsibility for hiring, corrective action, and performance reviews. Suggested preparation: use situation, action, and result to explain your decision-making, respectful communication, and follow-through without disclosing confidential details.

02

Sharpen your knowledge

Topics to prepare

  • Hybrid-cloud architecture trade-offs

    The role leads architectural decisions across on-premise, hybrid-cloud and cloud-native systems, balancing scalability, performance, robustness and fault tolerance. Practice suggestion: compare two architectures for an AI-enabled application and explain the trade-offs and failure points in each.

  • LLM adoption patterns

    The posting names RAG, agent workflows and secure prompt orchestration as patterns developed with Architecture teams. Practice suggestion: explain when each pattern would be appropriate for an application, including what could go wrong in production.

  • Secure AI and data usage

    Secure computing, model integration and secure data usage patterns are explicit areas of responsibility. Practice suggestion: review a hypothetical application design and identify where data access, model integration and prompt handling need security decisions.

  • AI software operationalization

    The manager oversees AI and machine-learning applications from proof of concept through production and post-production support. Practice suggestion: outline the decisions needed at each stage, including testing, deployment, monitoring and support ownership.

  • Operational reliability and incident diagnosis

    The role leads restoration and root-cause resolution for outages, including AI- and model-related failures. Practice suggestion: compare how you would investigate an application-performance failure versus a model-related failure, and how you would verify recovery.

  • Engineering management across teams

    The posting specifies coaching, career development, daily work direction, hiring, corrective action and performance reviews, alongside collaboration with Architecture, DevOps, Infrastructure and Security. Practice suggestion: map who should be involved in resolving a cross-team delivery blocker and how you would communicate a decision.

Skills to revise

  • Scope a hands-on AI proof of concept

    The manager both leads and contributes to POCs for applied AI, machine learning and GenAI. Practice suggestion: write a one-page POC plan for a hypothetical operational use case, with a testable objective, success measures and a decision on whether to proceed.

  • Perform a design and code review

    Design and code reviews must address development practices, model integration and secure data usage. Practice suggestion: annotate a sample full-stack or AI-enabled design with specific changes you would request before deployment.

  • Plan an automated production release

    The role partners with DevOps, Infrastructure and Security to improve CI/CD, MLOps and full-stack automation. Practice suggestion: sketch a release sequence for an AI-enabled application, showing testing, model deployment, failure handling and post-release checks.

  • Lead a model-related incident exercise

    Incident investigation, rapid restoration and root-cause resolution are stated duties. Practice suggestion: run a tabletop scenario in which an AI-enabled application starts producing faulty results; record triage steps, cross-team escalation, restoration criteria and follow-up actions.

  • Coach and prioritize a development team

    The manager directs daily work and holds regular 1:1s and career-development discussions. Practice suggestion: role-play a 1:1 about a developer's technical blocker, then draft a short priority update that explains the team's next steps to stakeholders.

04

Your preparation checklist

  • Map the stated qualifications to evidence

    Prepare a one-page list of examples for the required degree, 8+ years of related work, 4+ years of hands-on development, and 2+ years of leadership or management. Mark any gaps rather than assuming they are met.

  • Build a project walkthrough

    If you have a relevant example, outline the business objective, your hands-on and leadership contributions, proof-of-concept findings, production deployment, and measured outcome. The posting emphasizes taking AI-enabled software from concept through operationalization.

  • Practice a hybrid-cloud design exercise

    Sketch a hypothetical AI-enabled application for a clinical, operational, or administrative use. Explain secure data usage, model integration, and trade-offs in scalability, performance, robustness, and fault tolerance; treat it as a practice scenario, not a BILH project.

  • Prepare a design and code review example

    Bring a concise example of how you evaluated architecture, testing, and secure development choices. Be ready to discuss where RAG, agent workflows, or secure prompt orchestration would—and would not—fit.

  • Trace delivery and incident response

    Draw a simple path from development through CI/CD or MLOps to post-production support. Prepare an incident example covering investigation, restoration, root cause, and a lasting improvement, including model-related failure if applicable.

  • Document people and cross-team leadership

    Select examples of coaching, prioritizing work, and resolving a blocker with Architecture, DevOps, Infrastructure, or Security. Note your decision, how you communicated it, and the result; the role also has hiring and performance-review responsibility.

  • Write questions for the employer

    Ask which AI use cases are priorities, how the team evaluates a proof of concept for production, where hybrid-cloud or data-pipeline constraints arise, and how success is measured for the manager and team.

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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