OPT Director Of Financial Operations Jobs
Director of Financial Operations jobs on OPT require employers who can sponsor H-1B visa or support cap-exempt pathways, since your 12-month OPT window (29 months with STEM extension) is limited. These senior finance roles typically require CPA credentials, advanced degrees, and deep experience managing treasury, reporting, and compliance functions.
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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 982+ OPT Director Of Financial Operations Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Director Of Financial Operations Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship in Director Of Financial Operations
Target companies with a sponsorship track record
Search OFLC disclosure data for employers who have filed LCAs for finance director roles. Companies that have sponsored similar positions before are far more likely to sponsor again than those with no history.
Apply early if you have a STEM degree
A STEM-eligible degree in finance, economics, or a related quantitative field extends your OPT to 29 months. Starting your job search six to nine months before expiration gives you realistic runway to secure sponsorship.
Focus on large corporations and financial institutions
Enterprises with established HR and legal infrastructure sponsor most senior finance hires. Smaller firms often lack the internal resources to navigate H-1B petitions for a Director-level position effectively.
Clarify your authorization timeline upfront in applications
State your OPT status, current authorization end date, and STEM extension eligibility in your cover letter. Recruiters filling senior finance roles need to plan timelines, and ambiguity about work authorization typically ends conversations early.
Negotiate sponsorship commitment before accepting an offer
Get written confirmation that the employer will file H-1B sponsorship before your OPT expires. A verbal promise at the Director level carries real risk if the sponsoring manager changes roles or priorities shift.
Director Of Financial Operations OPT: Frequently Asked Questions
Can F-1 OPT students work as a Director of Financial Operations?
Yes. Director of Financial Operations is a specialty occupation that qualifies for both OPT work authorization and eventual H-1B sponsorship. Your F-1 OPT lets you work full-time in the role for 12 months, or up to 29 months if you hold a STEM-eligible degree in finance, accounting, economics, or a related quantitative field.
What degrees qualify a Director of Financial Operations for a STEM OPT extension?
Degrees in finance, accounting, economics, mathematics, statistics, and management information systems commonly qualify for the 24-month STEM extension. The degree must appear on the official STEM Designated Degree Program List, and your employer must be enrolled in E-Verify. Confirm your specific CIP code with your DSO before assuming eligibility.
How do I find Director of Financial Operations jobs that sponsor OPT or H-1B?
Migrate Mate is the recommended platform for F-1 OPT students searching for sponsorship-friendly senior finance roles. You can filter by roles open to OPT candidates and focus your applications on employers with a documented history of filing H-1B petitions for finance directors and similar positions.
Does a CPA license help with H-1B sponsorship for this role?
It significantly strengthens the petition. USCIS requires specialty occupations to demonstrate that a specific bachelor's degree or higher in a directly related field is the standard minimum for entry. A CPA license reinforces the degree-to-role nexus and helps employers justify the position classification to USCIS, which can reduce the risk of an RFE.
What happens to my OPT status if my employer restructures and eliminates the Director role?
You enter a 60-day grace period from the date your employment ends. During that window, you can transfer to a new employer using your existing OPT, return to school, or begin exploring a change of status. You cannot legally work during the grace period. Notify your DSO immediately so they can update your SEVIS record accurately.