LLMOps Architect

About Enterpret Enterpret is at the forefront of AI-native applications, unlocking the power of customer feedback for businesses. We centralize feedback from every channel and transform it into actionable insights that drive customer-centric decisions for teams at the world's leading companies like Perplexity, Notion, Canva, and Figma. Backed by investors such as Kleiner Perkins and Peak XV, we're redefining how businesses understand and act on the voice of their customers. About the Role As LLMOps Architect at Enterpret, you'll be responsible for how LLM models are fine-tuned, how prompts are managed, how we run evals, how we optimize for cost, and how we optimize for speed—both at the experimentation stage and in production. This is a foundational, high-ownership role where you'll work directly with the OpenAI and Anthropic teams (whom we partner with closely), as well as AWS (whom we partner with closely), to build world-class ML infrastructure. You'll work closely with ML researchers, backend engineers, and product teams to ensure our AI systems are resilient, secure, and cost-efficient as we grow 10x. Key success metrics include improving the speed of experimentation, time to productionization, and the quality of models. You'll report directly to the CTO. What You'll Do Design and evolve Enterpret's ML platform for training, serving, and retraining our encoders and LLM models using AWS/Terraform/OpenAI/Anthropic. Build CI/CD pipelines tailored for ML—including model versioning, testing, canary releases, rollbacks, and gated production deploys. Deploy and manage model serving systems for both real-time inference (e.g., tagging support tickets on the fly) and batch pipelines (e.g., analyzing historical product feedback). Set up observability for model performance and data drift—using Braintrust and custom alerts to catch issues before they affect customers. Lead incident response, root cause analysis, and postmortems for ML systems—ensuring uptime for insights that product teams rely on, alongside governance and security. Track and optimize cloud usage for ML workflows, making model delivery cost-aware and aligned with product usage. Implement governance and security across the stack—owning IAM, data access, auditability, and model explainability where needed. Partner with ML and product teams to productionize GenAI and AI models powering our Knowledge Graph and Adaptive Taxonomy engine, tackling problems on retrieval, encoder LLM fine-tuning, and reinforcement learning. Evaluate tools for model registry, feature stores, and orchestration—and build where needed to keep the feedback loop fast. Champion best practices in MLOps across the org—mentoring engineers and setting scalable foundations for the future. Act as a coach to our team of researchers who are transitioning into engineering, helping them self-serve their capabilities and self-service these tools rather than doing it yourself. What It Takes A minimum of 6 years' experience in MLOps and ML infrastructure, ideally with exposure to designing, deploying, and scaling machine learning systems in fast-paced, product-driven environments such as startups or high-growth companies. Deep expertise with AWS (SageMaker, EC2, EKS, S3, IAM), infrastructure-as-code (Terraform), and container orchestration (Docker, Kubernetes). Strong Python skills, with bonus points for Go, Bash, or Rust scripting where appropriate. Hands-on experience with CI/CD systems like GitHub Actions, ArgoCD, or Jenkins—especially for ML model delivery. Proven ability to monitor and maintain production ML systems, including model drift, latency, uptime, and alerting. Comfort with cloud cost optimization, resource provisioning, and auto-scaling for ML-heavy environments. Familiarity with model serving stacks and experimentation tools (MLflow, Langsmith, etc.). Bonus: exposure to GenAI workflows (LangChain, vector DBs, RAG), encoder/LLM model tuning, reinforcement learning, or responsible AI practices. Trac