MLOPs Architect

Quantum World Technologies Inc.; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

An established industry player is seeking a skilled MLOps/LLMOps Architect to design and implement scalable machine learning pipelines. This role involves developing observability strategies, optimizing model performance, and ensuring compliance with ethical AI guidelines. The ideal candidate will have extensive experience in MLOps and LLMOps, along with strong programming skills in Python and familiarity with cloud platforms. Join a dynamic team where your expertise will drive innovation and impact in the AI landscape, collaborating with cross-functional teams to achieve business objectives.

Qualifications

• 10-15 Jahre Erfahrung in MLOps und AI-Modellbereitstellung.
• Starke Kenntnisse in der Nutzung von Observability-Tools wie Arize.
• Erfahrung mit Cloud-Plattformen und Containerisierung.

Responsibilities

• Architektur und Implementierung von MLOps/LLMOps-Frameworks.
• Entwicklung von CI/CD-Pipelines zur Automatisierung der Modellbereitstellung.
• Zusammenarbeit mit Datenwissenschaftlern und Stakeholdern zur Ausrichtung der MLOps-Strategien.

Skills

MLOps

LLMOps

Model Performance Monitoring

Observability

Python

Cloud Platforms (AWS, Azure, GCP)

Performance Tuning

Model Optimization

Tools

Arize

Weights & Biases

TensorBoard

MLflow

Docker

Kubernetes

TensorFlow

PyTorch

Hugging Face

Job description

We are seeking an experienced MLOps/LLMOps Architect with deep expertise in building scalable, production-grade machine learning and generative AI pipelines. The ideal candidate will have strong experience in observability, model performance monitoring, and using tools such as Arize to ensure the reliability and scalability of AI/ML models in production. This role requires a strategic mindset and hands-on technical skills to design and implement robust MLOps/LLMOps frameworks, ensuring seamless model deployment, monitoring, and optimization.

Key Responsibilities:

• Architect and Implement MLOps/LLMOps Frameworks:
• Design and build scalable MLOps/LLMOps pipelines for model training, deployment, monitoring, and retraining.
• Establish automated CI/CD pipelines to streamline model development and deployment.
• Model Observability and Monitoring:
• Develop and implement model observability strategies using tools like Arize to track model performance, drift, and bias.
• Create real-time dashboards and alerts for proactive issue identification and resolution.
• Performance and Scalability:
• Ensure high availability, low latency, and scalability of deployed models.
• Optimize model inference and serving using best practices in distributed computing and cloud infrastructure.
• Manage and optimize compute costs for large-scale Gen AI models by implementing intelligent load balancing, autoscaling, and infrastructure tuning.
• Model Governance and Compliance:
• Establish frameworks for model versioning, auditing, and explainability to meet regulatory and business requirements.
• Ensure alignment with Responsible AI and ethical AI guidelines.
• Cross-Functional Collaboration:
• Partner with data scientists, ML engineers, platform teams, and business stakeholders to align MLOps strategies with business objectives.
• Provide technical leadership and mentorship to junior team members.

Required Skills and Qualifications :

• Experience: 10 to 15 years of experience in machine learning, MLOps, and AI model deployment in enterprise environments.
• MLOps/LLMOps Expertise: Strong background in MLOps and LLMOps, including model lifecycle management, monitoring, and automation.
• Observability Tools: Proficient in using observability platforms such as Arize, Weights & Biases, TensorBoard, MLflow, or similar tools.
• Cloud Platforms: Experience with cloud-based ML solutions (e.g., AWS, Azure, GCP).
• Programming: Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Containerization and Orchestration: Hands-on experience with Docker, Kubernetes, and distributed computing frameworks.
• Model Monitoring: Experience in detecting and mitigating model drift, bias, and data quality issues.
• Performance Tuning: Expertise in model optimization, inference acceleration, and efficient resource utilization.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Consulting

Industries

IT Services and IT Consulting