Senior MLOps Engineer – LLMOps

TRM is a blockchain intelligence company that’s on a mission to build a safer world for billions of people. We’re a lean, high-impact team tackling some of the world’s most critical challenges, ranging from human trafficking and financial fraud to terrorist financing. We are builders who power governments, financial institutions, and crypto companies when the clock is running and the consequences are real.

This is why every TRMer is a bet on our future and has the power to change our trajectory.

The AI Engineering Team is chartered with enabling next-generation AI applications
, with a special focus on Large Language Models (LLMs) and agentic systems. Our mission is to build robust pipelines, high-performance infrastructure, and operational tooling that allow AI systems to be deployed with speed, safety, and scale.

We manage petabyte-scale pipelines, serve models with millisecond-level latency, and provide the observability and governance needed to make AI production-ready. We’re also deeply involved in evaluating and integrating cutting-edge tools in the LLM and agent space — including open-source stacks, vector databases, evaluation frameworks, and orchestration tools that unlock TRM’s ability to innovate faster than the market.

As a Senior MLOps Engineer with a focus in LLMOps
, you’ll be at the core of building and scaling the technical infrastructure for AI/ML systems. You will:

• Build reusable CI/CD workflows for model training, evaluation, and deployment — integrating Langfuse, Git Hub Actions, and experiment tracking, etc.
• Automate model versioning, approval workflows, and compliance checks across environments.
• Build out a modular and scalable AI infrastructure stack — including vector databases, feature stores, model registries, and observability tooling.
• Partner with engineering and data science to embed AI models and agents into real-time applications and workflows.
• Continuously evaluate and integrate state-of-the-art AI tools (e.g. Lang Chain, Llama Index, vLLM, MLflow, Bento
  
  ML, etc.).
• Drive AI reliability and governance, enabling experimentation while ensuring compliance, security, and uptime.
• Build and enhance AI/ML Model Performance
• Ensure data accuracy, consistency and reliability, leading to better model training and inferencing
• Deploy infrastructure to support offline and online evaluation of LLMs and agents — including regression testing, cost monitoring, and human-in-the-loop workflows.
• Enable researchers to iterate quickly by providing sandboxes, dashboards, and reproducible environments.

• Write high-quality, maintainable software — primarily in Python, but we value engineering ability over language familiarity.
• Have a strong background in scalable infrastructure
  , including:
  • Containerization and orchestration (e.g. Docker, Kubernetes)
  • Infrastructure-as-code and deployment (e.g. Terraform, CI/CD pipelines)
  • Monitoring and logging frameworks (e.g. Datadog, Prometheus, Open Telemetry)
• Understand and implement ML Ops best practices
  , including:
  • Model versioning and rollback strategies
  • Automated evaluation and drift detection
  • Scalable model and agent serving infrastructure (e.g. vLLM, Triton, Bento
    
    ML)
• Deploy and maintain LLM and agentic workflows in production, including:
  • Monitoring cost, latency, and performance
  • Capturing traces for analysis and debugging
  • Optimizing prompt/response flows with real-time data access
• Demonstrate strong ownership and pragmatism
  , balancing infrastructure elegance with iterative delivery and measurable impact.

Learn about TRM Speed in this position:

• Rapid Issue Resolution.TRM Engineers identify and resolve critical onsite issues in minutes to hours, not weeks. We create virtual war rooms, implement fixes, and share lessons with both customer stakeholders and internal teams within 48 hours.
• Navigating Bureaucracy.We anticipate and address procedural hurdles, build trust with key stakeholders, and find alternative pathways to approvals. This keeps projects moving even in complex environments.
• Efficient Knowledge Transfer.Engineers document and share updates in real time, ensuring the entire team—onsite and remote—has full visibility into plans, blockers, and…