OPT Operations Consultant Jobs
Operations Consultant roles on OPT require a specialty occupation connection to your degree, typically in business, industrial engineering, or supply chain. Most positions offer 12 months of OPT, with STEM OPT extensions available if your employer enrolls in E-Verify. Cap-exempt employers and consulting firms frequently sponsor H-1B visa transfers.
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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 1,414+ OPT Operations Consultant Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as an Operations Consultant
Target consulting firms with established OPT pipelines
Large and mid-size consulting firms regularly hire OPT students and have structured visa sponsorship processes. Prioritize firms that have filed H-1B visa petitions for operations roles before, which signals familiarity with the sponsorship pathway.
Align your degree field to the job description
Operations Consultant roles qualify as specialty occupations when tied to industrial engineering, supply chain, or business analytics degrees. Ensure your resume and cover letter draw an explicit connection between your academic background and the specific operational responsibilities listed.
Apply early to protect your OPT timeline
Operations consulting hiring cycles often run four to six months. Applying early gives hiring managers time to assess sponsorship feasibility before your OPT authorization expires, reducing pressure on both sides during the offer and onboarding process.
Ask about E-Verify enrollment before accepting an offer
STEM OPT extensions require your employer to be enrolled in E-Verify. Confirming this early prevents surprises after you accept. If the employer is not enrolled, ask whether they are willing to complete enrollment before your extension filing date.
Emphasize process improvement and quantifiable impact
Operations Consultant hiring decisions often hinge on demonstrated analytical outcomes. Quantify efficiency gains, cost reductions, or cycle time improvements from past roles or projects. Concrete results reduce hesitation around sponsorship by making your value case straightforward.
Understand your reporting requirement obligations
OPT students must report employer changes to their DSO within ten days. If you move between consulting engagements or client sites, confirm whether each arrangement constitutes a new employer record requiring an update to your SEVIS record to stay in status.
Operations Consultant OPT: Frequently Asked Questions
Does an Operations Consultant role qualify for OPT work authorization?
Yes, Operations Consultant positions generally qualify as specialty occupations under OPT when the role requires applying knowledge from a specific field, such as industrial engineering, supply chain management, or business analytics. Your degree must have a direct and logical connection to the operational responsibilities in the job description. Roles focused on general administrative tasks with no clear degree requirement are less likely to qualify.
Can I get a STEM OPT extension as an Operations Consultant?
You can qualify for a 24-month STEM OPT extension if your degree is in an eligible STEM field, such as industrial engineering, operations research, or management information systems, and your employer is enrolled in E-Verify. Business administration degrees are not always on the STEM designation list, so confirm your specific CIP code with your DSO before counting on the extension.
How do I find Operations Consultant jobs that are open to OPT candidates?
Migrate Mate is built specifically for international students on OPT and visa-requiring workers, so every Operations Consultant listing on the platform is from employers open to sponsoring or hiring on work authorization. Browsing there filters out the noise of applying to roles where OPT status is an automatic disqualification, which saves significant time during a tight authorization window.
Will consulting firms sponsor H-1B visas after my OPT ends?
Many mid-size and large consulting firms do sponsor H-1B visas for strong performers, particularly in operations, strategy, and supply chain practices. Sponsorship is not guaranteed and depends on firm policy, your performance, and the H-1B lottery outcome. It is worth asking about sponsorship history during the interview process rather than waiting until your OPT expiration approaches.
Does client-site consulting work affect my OPT status?
It can, depending on the arrangement. Under OPT, you must work for the employer listed in SEVIS. If you are placed at a client site as a direct employee of the consulting firm, your status is generally unaffected. If the arrangement looks more like self-employment or independent contracting, it may not comply with OPT regulations. Confirm the employment structure with your DSO before accepting project-based or third-party placements.