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Saxon Global Logo
Sr. Machine Learning Engineer

Saxon Global

 

Irving, TX, USA

Posted On: 30+ days ago
Experience: 10+ years
Availability: Remote
Openings: 1
Category: Sr. Machine Learning Engineer
Tenure: No Preference/Any
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Description

Job Description

This needs to be a Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. 

Must Haves:
• 10+ years experience building production-grade ML systems at scale
• Strong LLM, GenerativeAI, RAG deployment experience
• Expertise designed systems in cloud environments (AWS, Axure or GCP)
• Hands-on work with Kubernetes, containerizsiton, and scalable inference systems
• Experience designing agentic systems and tool orchestration frameworks
• Ability to implement and govern MCP servers or structured architectures
• Strong python background

Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. This is a senior technical leadership role for someone who thrives at the intersection of:


• Large-scale distributed ML systems
• LLM and RAG architectures
• Agentic AI frameworks and tool orchestration
• Enterprise platform engineering
You will shape the long-term AI platform strategy and establish technical standards that impact millions of users.

What You’ll Do Architect Scalable AI Platforms
• Define reference architecture for LLM, ML, and agent-based systems across products.
• Design high-availability, low-latency inference platforms for global scale.
• Establish reusable platform components for model lifecycle, deployment, and monitoring.

Lead Agentic AI & Tool Ecosystems
• Architect multi-step, reasoning-driven agent systems.
• Design orchestration patterns for tool use, API invocation, and structured function calling.
• Lead implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management.
• Define guardrails, permissions, and audit mechanisms for enterprise-safe AI systems.

Elevate Engineering Standards
• Set best practices for MLOps, CI/CD, observability, and system reliability.
• Embed Responsible AI principles across platform architecture.
• Mentor senior engineers and influence technical direction across teams

Education

Any Graduate

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