Senior MLOps Engineer

Location: Seattle, WA or McLean, VA or Remote

Trase Systems is AI, Uncomplicated. Trase empowers enterprise leaders to harness the full potential of AI without the associated complexity and risks. We are an end-to-end solution for deploying, managing, and optimizing AI in the enterprise. Our platform specializes in bridging the “last mile” of AI adoption, unlocking AI's full potential while driving efficiency and significant cost savings.

Are you passionate about developing and refining machine learning systems? Do you thrive in architecting and optimizing ML models that drive innovation and results? If so, we have an exciting opportunity for you as a Senior MLOps Engineer at Trase Systems.

In this role, you will be instrumental in advancing our ML systems, focusing on model training, pipeline development, and the fine-tuning of large language models (LLMs) to ensure peak performance. As part of our team, you will contribute directly to the continuous improvement of our ML models, working to maximize impact and align with evolving business needs.

• Own the technical vision, strategy, and end-to-end architecture for Trase’s MLOps platform, ensuring scalability, reliability, security, and cost-efficiency.  
• Architect and build a sophisticated CI/CD/CT ecosystem to automate the entire ML lifecycle, from data validation to production monitoring.  
• Lead the design of scalable and resilient ML infrastructure using IaC (Terraform) and container orchestration (Kubernetes) on a major cloud platform.  
• Establish MLOps best practices, including frameworks for version control, experiment tracking, model governance, and responsible AI.  
• Implement a robust monitoring and alerting framework to track model performance, detect drift, and ensure the reliability of production ML services.  
• Serve as the organization's thought leader on MLOps, mentoring engineers, and driving cross-functional alignment on platform strategy and best practices.  
• Define the multi-year roadmap for Trase’s MLOps ecosystem in alignment with business and product strategy.
• Anticipate emerging trends (LLMOps, autoML, multi-cloud, federated learning) and guide the org to adopt them proactively.
• Define patterns for operating large-scale LLMs and multi-modal AI in production with efficiency and compliance.
• Solve highly ambiguous, large-scale ML deployment challenges where no precedent exists, defining best practices for the org.

• 10+ years in software/infrastructure engineering, with 5+ years in a senior/lead MLOps, ML Infrastructure, or Platform role.  
• Expertise in designing and operating scalable, production-grade ML systems on AWS, GCP, or Azure.
• Mastery of Docker and Kubernetes for managing production ML workloads.  
• Proven experience managing complex infrastructure as code (IaC) with tools like Terraform.  
• Deep experience architecting CI/CD/CT pipelines for complex ML workflows (e.g., GitHub Actions, Jenkins).  
• Strong Python programming skills for infrastructure automation, tooling, and services.  
• Experience architecting solutions across the full ML lifecycle, from experiment tracking to advanced deployment patterns and monitoring.  
• Exceptional communication skills to articulate complex architectural strategy to stakeholders at all levels.  
• Familiarity with modern MLOps tools like MLflow, Kubeflow, SageMaker, or Vertex AI.  
• Experience with the operational challenges of LLMs, including fine-tuning pipelines, RAG systems, and vector databases.  

• 100% employer-paid, comprehensive health care including medical, dental, and vision for you and your family. 
• Paid maternity and paternity for 14 weeks at employees' normal pay. 
• Unlimited PTO, with management approval. 
• Opportunities for professional development and continued learning with educational reimbursements. 
• Optional 401K, FSA, and equity incentives available. 
• Mental health benefits through TARA Mind.

Some travel is required.

If you want to be on the cutting edge of technology, building AI solutions for the future, and are up for a challenge, let’s talk!

Salary Range: $200,000-250,000. This represents the typical salary range for this position based on experience, skills, and other factors.