Job Title: ML Architect - Contractor

Location: Remote (United States)

Duration: 4 Months (Contract to Hire)

Start Date: Immediate

Key Responsibilities

Architect and design a scalable AI/automation solution to streamline manual processes within the company's internal operations.
Lead the end-to-end solution design, including data pipelines, model lifecycle management, and system integration, ensuring the architecture is optimized for performance, scalability, and maintainability.
Leverage advanced techniques such as constrained optimization to improve resource allocation and replace manual processes with an intelligent automation system.
Collaborate closely with SMEs and business stakeholders to understand operational requirements and design an AI/ML system that reflects their expertise while ensuring it integrates seamlessly into broader enterprise systems.
Define the architectural standards and best practices for the deployment of machine learning models, working closely with ML Engineers and Data Engineers to ensure alignment with business and technical objectives.
Establish frameworks for automated resource allocation, data processing, and model orchestration, optimizing operational efficiency and service quality.

Required Qualifications

Proven experience as an ML Architect, with a track record of designing and implementing large-scale machine learning and AI systems in production environments.
Deep expertise in machine learning algorithms, AI models, and frameworks (e.g., TensorFlow, PyTorch), with an ability to guide the design and implementation of complex systems.
Extensive experience in architecting data pipelines and working with large datasets, with strong proficiency in SQL and cloud-based data warehouse solutions (e.g., Azure preferred).
Expertise in architecting AI-driven automation solutions, with a focus on resource allocation, scheduling optimization, and efficient system orchestration.
Hands-on experience with cloud platforms (preferably Azure) for deploying machine learning models, managing data storage, and building data infrastructures.
Strong programming skills in Python, with additional expertise in languages like Java, Scala, or C++ for designing production-level architectures.
Experience in architecting RESTful APIs and microservices for integrating machine learning models into enterprise systems.
Advanced knowledge of algorithms, data structures, and distributed systems for building highly efficient and scalable ML solutions.
Experience with DevOps practices, including Git, CI/CD pipelines, containerization (Docker, Kubernetes), and model lifecycle management.
Excellent communication and leadership skills, with the ability to work cross-functionally with SMEs, data engineers, ML engineers, and other stakeholders.

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