OPT Account Operations Manager Jobs
Account Operations Manager roles sit at the intersection of client management and process coordination, making them a strong fit for OPT students with business, management, or operations degrees. Most positions qualify as specialty occupations for H-1B visa sponsorship, and many employers in tech, finance, and SaaS actively hire OPT candidates.
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Get Access To All JobsINTRODUCTION
The AI & ML Operations Engineer designs, builds, and maintains the engineering processes and platform capabilities needed to deploy, monitor, govern, and maintain deployed AI/ML solutions at scale. The role converts validated analytical work into reliable production solutions and manages the operational lifecycle of models and related services while meeting enterprise privacy, security, and support standards.
The engineer partners with data scientists, data engineers, central cloud and infrastructure teams, API and Interoperability teams, Cybersecurity, service owners, and relevant engineering teams to deliver maintainable solutions. The role remains hands-on in engineering, testing, deployment, and troubleshooting while using enterprise-managed capabilities for production data movement, integration, and infrastructure. Scientific method validation remains with the Data Scientist and designated reviewers; this position implements the operational controls for deployed solutions.
This role is part of a new AI & Automation engineering team and will directly design, build, deploy, and support production AI applications and services. Work may include agents, retrieval-augmented generation, orchestration, APIs and integrations, automated evaluation and testing frameworks, observability, monitoring, release and rollback processes, and model/application lifecycle management.
Responsibilities
Model deployment and orchestration. Deploy batch and real-time models and related AI services using approved CI/CD, containers, APIs, and workflow patterns. Build and maintain assigned model deployment and runtime components and coordinate dependencies with enterprise platform owners.
Release management. Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services. Implement retraining or recalibration workflows using criteria defined with data scientists and the required validation and release approvals.
ML platform capabilities. Implement, integrate, configure, and operate approved feature stores, model registries, experiment tracking, and related ML lifecycle capabilities with enterprise platform owners. Partner with Cloud & AI Deployment Engineering and central cloud teams on compute, storage, networking, and deployment.
Deployed-model governance. Implement approved lifecycle controls, access restrictions, lineage, audit trails, change records, and release approvals for deployed models. Maintain traceability across data, code, model, and configuration versions.
Monitoring and reliability. Implement logging, alerts, and monitoring for model performance, drift, data quality, latency, availability, and errors. Work with data scientists, service owners, and relevant engineering teams on measures, thresholds, incident response, and service objectives.
Data and API integration. Work with central data-engineering teams on managed data pipelines and with enterprise API and Interoperability teams on real-time and application integrations, including MuleSoft or other approved API capabilities. Coordinate monitoring, recovery checkpoints, restart procedures, security, and support ownership.
Enterprise support and maintainability. Ensure production solutions have documented dependencies, operational procedures, escalation paths, and ongoing ownership before release. Arrange support with the responsible enterprise teams, including 24/7 coverage where required for critical services, rather than relying on an individual project owner.
AI application operations. Operate assigned LLM, retrieval-based, and agent applications using automated quality checks, model/tool tracing, prompt and configuration versioning, and usage/cost monitoring within the common production lifecycle.
Security and engineering standards. Apply enterprise privacy, security, encryption, role-based access, and sensitive-data controls. Follow common infrastructure-as-code, pull-request, and deployment practices with the responsible teams; bring needed tooling or design exceptions through established review processes.
Collaboration and improvement. Help scientists and partner teams move prototypes into supported production solutions. Review code, tests, and documentation, resolve incidents across dependencies, provide constructive technical feedback, and improve reliability, reproducibility, performance, and efficiency.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Systems, or a related field, or an equivalent combination of education and relevant experience.
- At least three years of experience in software engineering, DevOps, or MLOps, including work with production systems.
- Proficiency in Python or another modern programming language, such as Java or Go, sufficient to build, test, troubleshoot, and maintain production components.
- Hands-on experience deploying and operating batch and/or real-time machine-learning models in production.
- Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps; source control, code review, automated testing, and controlled releases.
- Hands-on experience with containerization and orchestration technologies using enterprise-approved platforms and tools.
- Experience in cloud and/or hybrid environments such as AWS, Azure, or GCP.
- Working knowledge of SQL and experience with large datasets and data-workflow orchestration; ability to integrate model services with enterprise-managed pipelines and APIs.
- Experience implementing logging, metrics, monitoring, alerts, and operational diagnostics and investigating failures across model, application, data, and infrastructure dependencies.
- Understanding of security best practices, role-based access control, secrets management, and handling sensitive data.
- Ability to independently deliver assigned engineering work, communicate technical tradeoffs, and collaborate with data scientists, clinicians, data/API teams, cloud/DevOps, security, and support functions.
PREFERRED QUALIFICATIONS
- Experience in healthcare, health technology, or another regulated industry handling PHI/PII.
- Familiarity with EHR/EMR integration and healthcare data standards such as HL7/FHIR, ICD, CPT, LOINC, or SNOMED.
- Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML, including model registries, feature stores, and experiment tracking.
- Experience with ETL/ELT, data warehouse or lakehouse platforms, enterprise API platforms, infrastructure as code, and formal production-support practices.
- Experience monitoring model drift, fairness, and explainability and implementing healthcare AI governance controls.
- Experience operating LLM, retrieval-based, or agent applications, including quality evaluation, tracing, prompt/configuration management, and cost monitoring.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
INTRODUCTION
The AI & ML Operations Engineer designs, builds, and maintains the engineering processes and platform capabilities needed to deploy, monitor, govern, and maintain deployed AI/ML solutions at scale. The role converts validated analytical work into reliable production solutions and manages the operational lifecycle of models and related services while meeting enterprise privacy, security, and support standards.
The engineer partners with data scientists, data engineers, central cloud and infrastructure teams, API and Interoperability teams, Cybersecurity, service owners, and relevant engineering teams to deliver maintainable solutions. The role remains hands-on in engineering, testing, deployment, and troubleshooting while using enterprise-managed capabilities for production data movement, integration, and infrastructure. Scientific method validation remains with the Data Scientist and designated reviewers; this position implements the operational controls for deployed solutions.
This role is part of a new AI & Automation engineering team and will directly design, build, deploy, and support production AI applications and services. Work may include agents, retrieval-augmented generation, orchestration, APIs and integrations, automated evaluation and testing frameworks, observability, monitoring, release and rollback processes, and model/application lifecycle management.
Responsibilities
Model deployment and orchestration. Deploy batch and real-time models and related AI services using approved CI/CD, containers, APIs, and workflow patterns. Build and maintain assigned model deployment and runtime components and coordinate dependencies with enterprise platform owners.
Release management. Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services. Implement retraining or recalibration workflows using criteria defined with data scientists and the required validation and release approvals.
ML platform capabilities. Implement, integrate, configure, and operate approved feature stores, model registries, experiment tracking, and related ML lifecycle capabilities with enterprise platform owners. Partner with Cloud & AI Deployment Engineering and central cloud teams on compute, storage, networking, and deployment.
Deployed-model governance. Implement approved lifecycle controls, access restrictions, lineage, audit trails, change records, and release approvals for deployed models. Maintain traceability across data, code, model, and configuration versions.
Monitoring and reliability. Implement logging, alerts, and monitoring for model performance, drift, data quality, latency, availability, and errors. Work with data scientists, service owners, and relevant engineering teams on measures, thresholds, incident response, and service objectives.
Data and API integration. Work with central data-engineering teams on managed data pipelines and with enterprise API and Interoperability teams on real-time and application integrations, including MuleSoft or other approved API capabilities. Coordinate monitoring, recovery checkpoints, restart procedures, security, and support ownership.
Enterprise support and maintainability. Ensure production solutions have documented dependencies, operational procedures, escalation paths, and ongoing ownership before release. Arrange support with the responsible enterprise teams, including 24/7 coverage where required for critical services, rather than relying on an individual project owner.
AI application operations. Operate assigned LLM, retrieval-based, and agent applications using automated quality checks, model/tool tracing, prompt and configuration versioning, and usage/cost monitoring within the common production lifecycle.
Security and engineering standards. Apply enterprise privacy, security, encryption, role-based access, and sensitive-data controls. Follow common infrastructure-as-code, pull-request, and deployment practices with the responsible teams; bring needed tooling or design exceptions through established review processes.
Collaboration and improvement. Help scientists and partner teams move prototypes into supported production solutions. Review code, tests, and documentation, resolve incidents across dependencies, provide constructive technical feedback, and improve reliability, reproducibility, performance, and efficiency.
BASIC QUALIFICATIONS
- B.S./B.A. in Computer Science, Engineering, Information Systems, or a related field, or an equivalent combination of education and relevant experience.
- At least three years of experience in software engineering, DevOps, or MLOps, including work with production systems.
- Proficiency in Python or another modern programming language, such as Java or Go, sufficient to build, test, troubleshoot, and maintain production components.
- Hands-on experience deploying and operating batch and/or real-time machine-learning models in production.
- Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps; source control, code review, automated testing, and controlled releases.
- Hands-on experience with containerization and orchestration technologies using enterprise-approved platforms and tools.
- Experience in cloud and/or hybrid environments such as AWS, Azure, or GCP.
- Working knowledge of SQL and experience with large datasets and data-workflow orchestration; ability to integrate model services with enterprise-managed pipelines and APIs.
- Experience implementing logging, metrics, monitoring, alerts, and operational diagnostics and investigating failures across model, application, data, and infrastructure dependencies.
- Understanding of security best practices, role-based access control, secrets management, and handling sensitive data.
- Ability to independently deliver assigned engineering work, communicate technical tradeoffs, and collaborate with data scientists, clinicians, data/API teams, cloud/DevOps, security, and support functions.
PREFERRED QUALIFICATIONS
- Experience in healthcare, health technology, or another regulated industry handling PHI/PII.
- Familiarity with EHR/EMR integration and healthcare data standards such as HL7/FHIR, ICD, CPT, LOINC, or SNOMED.
- Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML, including model registries, feature stores, and experiment tracking.
- Experience with ETL/ELT, data warehouse or lakehouse platforms, enterprise API platforms, infrastructure as code, and formal production-support practices.
- Experience monitoring model drift, fairness, and explainability and implementing healthcare AI governance controls.
- Experience operating LLM, retrieval-based, or agent applications, including quality evaluation, tracing, prompt/configuration management, and cost monitoring.
COMPENSATION
- $109,449.60 - $154,523.20 Annual
See all 1,043+ OPT Account Operations Manager Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as an Account Operations Manager
Target employers with H-1B sponsorship history
Companies that have sponsored H-1B visa workers before are far more likely to do it again. Search OFLC disclosure data to identify employers who have filed for Account Operations or similar operations roles in the past two years.
Frame your degree as a specialty occupation match
Account Operations Manager roles typically require a degree in business administration, operations management, or a related field. When applying, explicitly connect your degree to the role requirements in your resume and cover letter.
Clarify your OPT timeline upfront with recruiters
Tell recruiters your OPT end date and STEM extension eligibility early. This prevents late-stage surprises and helps employers plan H-1B sponsorship timelines around your authorization window without unnecessary delays.
Prioritize mid-size and enterprise SaaS companies
Mid-size SaaS and enterprise tech companies frequently hire Account Operations Managers and have established immigration processes. These employers are more likely to have legal infrastructure already in place to support OPT and H-1B transitions.
Quantify process improvements and cross-functional impact
Hiring managers for operations roles care about measurable outcomes. Highlight specific improvements you drove, such as reduced turnaround times or improved account retention, to stand out and justify sponsorship investment.
Use Migrate Mate to focus on sponsorship-ready employers
Browsing general job boards wastes OPT time on roles with employers who won't sponsor. Migrate Mate filters for companies actively open to OPT and visa sponsorship, so every application targets a realistic opportunity.
Account Operations Manager OPT: Frequently Asked Questions
Do Account Operations Manager roles qualify for OPT?
Yes. Account Operations Manager positions typically require a bachelor's degree in business administration, operations management, or a related field, which satisfies the specialty occupation standard. Students on F-1 OPT with a relevant degree are generally authorized to work in this role without additional employer filings.
Am I eligible for a STEM OPT extension as an Account Operations Manager?
Possibly. STEM OPT eligibility depends on your degree's CIP code, not just the job title. If your degree is in a STEM-designated field such as information systems, industrial engineering, or applied mathematics, and the role is at an E-Verify employer, you may qualify for a 24-month extension beyond your initial 12 months.
How do I find Account Operations Manager jobs that sponsor visas?
Migrate Mate is built specifically for this. Rather than sorting through thousands of postings from employers who won't sponsor, Migrate Mate surfaces Account Operations Manager roles from companies with a documented history of OPT and H-1B sponsorship, so you can apply with confidence that the employer is already open to your situation.
Will employers sponsor H-1B visas for Account Operations Manager roles?
Many do, particularly in the SaaS, fintech, and enterprise technology sectors where this role is common. The key factor is whether the employer has filed LCAs for similar positions before. Account Operations Manager roles have been consistently approved as specialty occupations by USCIS, which makes the sponsorship case straightforward for prepared employers.
What happens if my OPT expires before my H-1B is approved?
If your employer files your H-1B petition before your OPT expires and you have a timely filed EAD extension on record, you may be covered under cap-gap protection through September 30 of that year. You should work with your DSO and employer's immigration counsel to ensure there is no gap in your work authorization during the transition period.