We’re seeking a Principal Machine Learning Engineer to lead the evolution of a large-scale AI and machine learning platform, driving the architecture, deployment, and operation of both conventional ML systems and next-generation agentic AI solutions.
This is a hands-on leadership role for someone who still enjoys coding but can also define technical strategy, make architecture decisions, and provide direction across a growing engineering function. You’ll own the platform roadmap, establish engineering best practices, and ensure AI systems are secure, scalable, observable, and production-ready.
What you’ll be doing
- Define and lead the architecture for ML and agentic AI platforms.
- Design and own MLOps and LLMOps practices across production environments.
- Build standards for CI/CD, Infrastructure as Code, deployment automation, and platform reliability.
- Lead the implementation of agent-to-agent communication and orchestration frameworks.
- Drive Azure-based AI infrastructure decisions and platform evolution.
- Integrate modern AI capabilities with complex enterprise and legacy systems.
- Mentor engineers and act as the senior escalation point for platform and production issues.
What we’re looking for
- 8+ years’ experience in Machine Learning Engineering, Platform Engineering, MLOps, or AI Infrastructure.
- Strong Python expertise and a track record of shipping production ML systems.
- Deep Azure experience (AWS also considered).
- Strong knowledge of Terraform, Docker/Kubernetes, CI/CD, and Infrastructure as Code.
- Experience with MLOps, LLMOps, observability, monitoring, and production support.
- Hands-on experience with agentic AI, multi-agent systems, MCP, A2A, or similar orchestration frameworks.
- Experience working within enterprise environments where integration with existing systems is critical.
This is an excellent opportunity for a Staff or Principal-level engineer who wants to shape the future of enterprise AI while remaining close to the technology.
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