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3 results for Aiml Engineer in Fort Worth, TX

ML OPS AI ENGINEER II

Coppell, TX
onsite
Temporary / Contract
57.4655 - 66.539 USD / Hourly
We are looking for an ML OPS AI Engineer II to support the delivery of machine learning solutions from development through live production in Coppell, Texas. This Long-term Contract opportunity is ideal for a hands-on engineer who can strengthen ML infrastructure, improve deployment reliability, and partner closely with AI teams to operationalize models at scale. The role focuses on building repeatable systems, increasing observability, and ensuring model workflows remain efficient, stable, and cost-conscious across cloud-based environments. Responsibilities: • Lead the end-to-end operationalization of machine learning models, moving solutions from experimentation into dependable production environments. • Develop and support ML infrastructure, automated pipelines, and deployment frameworks that improve reliability and reduce manual effort. • Create and manage containerized workloads using Docker and coordinate production services through Kubernetes. • Establish and maintain CI/CD processes for model training, packaging, testing, and release management. • Implement tools and standards for experiment tracking, feature lineage, and model version control to enable reproducibility. • Build monitoring solutions that surface system health, model behavior, and data drift, helping teams respond quickly to production issues. • Provision and optimize cloud and compute resources to support both training and inference workloads effectively. • Improve scalability, operational visibility, and cost efficiency across deployed AI services. • Partner with data scientists and ML engineers to simplify deployment pathways and align platform capabilities with model development needs.
2026-09-02T00:00:00Z

AI Engineer

Coppell, TX
onsite
Temporary / Contract
70 - 75 USD / Hourly
Seeking an AI Engineer to design, implement, and optimize AI-powered solutions that improve business processes, knowledge management, and operational efficiency. This role will work closely with technical and business teams to develop generative AI applications, build retrieval-based solutions, establish AI best practices, and ensure secure and compliant use of AI technologies. POSITION TITLE: AI EngineerLOCATION: Coppell, TX (Onsite 5 days)DURATION: 6-12 MonthsSALARY: $65-70/hour RESPONSIBILITIES<ul><li>Design, develop, and optimize AI-powered applications and workflows using Large Language Models (LLMs) and generative AI technologies.</li><li>Build and maintain prompt libraries, prompt evaluation frameworks, and best practices to improve AI accuracy, consistency, and user adoption.</li><li>Develop Retrieval-Augmented Generation (RAG) solutions that connect AI platforms to enterprise systems, databases, and document repositories.</li><li>Partner with business stakeholders to identify use cases and implement AI solutions that drive process improvements and operational efficiencies.</li><li>Establish AI governance, security controls, testing procedures, and compliance standards for production deployments.</li></ul>
2026-09-01T00:00:00Z

Computer Vision - AI Engineer

Coppell, TX
onsite
Temporary / Contract
57.4655 - 66.539 USD / Hourly
We are looking for a Computer Vision - AI Engineer to join a team in Coppell, Texas, on a Long-term Contract assignment. In this role, you will design and refine vision-based AI solutions that support real-world image and video use cases, while partnering with cross-functional teams to move ideas from research into production. The position is ideal for someone who combines strong machine learning expertise with practical understanding of hardware constraints and model performance in live environments. Responsibilities: •Design, build, and enhance computer vision and machine learning models for use cases involving object detection, image segmentation, classification, and video-based analysis. •Compare modeling approaches, assess trade-offs across accuracy, speed, and scalability, and recommend fit-for-purpose solutions aligned with business goals. •Train and adapt deep learning models using frameworks such as PyTorch or TensorFlow, applying techniques like transfer learning and optimization for efficient performance. •Establish evaluation methods, track key performance measures, investigate error patterns, and iterate on models to improve reliability and response time. •Incorporate practical considerations related to cameras, sensors, lighting conditions, and edge hardware when developing and tuning solutions. •Create and support data preparation, annotation, and validation workflows that enable consistent experimentation and dependable deployment. •Work closely with software, hardware, and MLOps partners to transition models from proof of concept into production-ready applications. •Contribute to deployment readiness by helping optimize models for inference and operational use in resource-constrained environments.
2026-09-03T00:00:00Z