Senior MLOps Lead - Build Production ML (Fort Worth)

Invictus Strategy & Solutions is a Service-Disabled Veteran-Owned Small Business (SDVOSB) providing strategic workforce solutions to mission-critical government and commercial operations. From cleared federal programs to complex industrial projects, we deliver top-tier professionals who drive performance, safety, and results. Our commitment to operational excellence and customer alignment makes us the partner of choice for organizations seeking agile and reliable talent solutions.SummaryInvictus Strategy & Solutions is seeking a Senior Machine Learning Engineer and MLOps POD Lead to join our growing technical delivery team in Fort Worth, Texas. This on-site role requires strong hands-on experience designing, deploying, and operating production grade machine learning systems integrated with enterprise data platforms.The selected candidate will lead a small delivery pod of three to five engineers and data scientists responsible for building and operating scalable machine learning pipelines. This role combines hands on MLOps execution with technical leadership, ensuring machine learning systems operate reliably across commercial and government environments with varying regulatory, security, and operational requirements.This role requires an engineer who remains directly involved in architecture, pipeline design, and production operations while guiding a small team responsible for delivery outcomes.Key ResponsibilitiesMLOps Architecture and ExecutionDesign, deploy, and operate end to end machine learning pipelines supporting large scale datasets integrated with enterprise data lakes and data warehousesBuild and maintain production grade MLOps systems across cloud platforms including Azure, AWS, and GCP with primary emphasis on Microsoft AzureImplement CI and CD pipelines supporting model training, versioning, deployment, and lifecycle managementUtilize MLflow for experiment tracking, model registry management, and model lifecycle governanceMonitor model performance, data drift, and system reliability across production environmentsEnsure machine learning services meet defined reliability and SLA expectationsCollaborate with Data Engineering teams to integrate ML pipelines with ETL workflows, feature engineering pipelines, and enterprise data platformsDeploy and manage ML workloads on Kubernetes based environmentsTechnical Leadership and DeliveryLead a delivery pod of three to five engineers and data scientists responsible for building and operating ML systemsProvide technical guidance, mentorship, and code review support to team membersTranslate business, operational, and regulatory requirements into scalable ML system architecturesOwn delivery outcomes across commercial and public sector engagements while maintaining quality, security, and compliance requirementsSecurity, Compliance, and Responsible AISupport machine learning implementations aligned with applicable standards including the NIST AI Risk Management FrameworkEnsure secure handling of sensitive data including healthcare, bioscience, or government datasetsSupport governance and operational oversight for machine learning lifecycle managementQualificationsBachelor's degree in Computer Science, Data Science, Engineering, or a related discipline, or equivalent professional experienceU.S. Citizenship with the ability to obtain and maintain a government security clearanceMinimum seven years of experience in machine learning engineering or MLOps supporting production systemsStrong proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar toolsHands on experience designing and deploying machine learning systems in cloud environments including Azure, AWS, or GCP, with demonstrated depth in Microsoft Azure environmentsExperience implementing CI and CD pipelines for machine learning workflowsHands on experience supporting enterprise data platforms including data lakes, data warehouses, and ETL pipelinesExperience deploying or operating workloads on Kubernetes based platformsStrong foundation in software engineering best practices including version control, automated testing, and documentationPreferredExperience supporting machine learning systems for commercial clients or federal or state government programsPrior technical leadership experience guiding small engineering teamsExperience deploying or operating ML systems in regulated cloud environments including Azure GovernmentFamiliarity with infrastructure as code tools such as TerraformExperience with AI governance frameworks, model risk management, or ethical AI practicesRelevant certifications may include: Microsoft Azure AI Engineer Associate, Microsoft Azure Data Scientist Associate, AWS Certified Machine Learning Specialty, or TensorFlow Developer CertificateCompensation and BenefitsEmployer-paid medical, dental, and vision insurance401(k) with company matchPaid time off and holidaysProfessional development and certification reimbursementInvictus Strategy & Solutions is unable to provide sponsorship at this time. All applicants must be legally authorized to work in the United States without current or future sponsorship requirements.EEO StatementInvictus Strategy & Solutions is an equal opportunity employer. All qualified applicants will receive consideration without regard to any protected characteristic.J-18808-Ljbffr