• Build and maintain production ML systems: Contribute to existing ML-based services, from training pipelines to production performance optimizations. • Own services end-to-end: Design, build and maintain production services - from a PoC all the way to a functional cloud deployment. • Partner with product and data science: Work with PMs, data scientists, and other engineers to translate research and business questions into shippable ML. • Understand the platform: To facilitate the above, you’ll work in Databricks, AWS and other tooling to be able to analyze, understand and present the data, metrics and insights needed to help make the best decisions for the platform. • Contribute to the team's standards: Review not just code but the ML side of the work as well - evaluation standards, model documentation etc. • Leverage AI tooling: Use Claude Code and agentic programming tools to accelerate the boilerplate so you can focus on the modelling decisions that actually matter.

• Develop and maintain a solid infrastructure within the Data Science team focusing on DevOps processes • Establish and extend tools to automate the Data Science Tech Stack to enhance MLOps processes • Ensure reliable and scalable deployment foundation for ML models • Collaborate effectively with stakeholders to achieve business goals • Build and maintain infrastructure as code using Terraform

• Pushing the architecture further • Helping with domain-specialized agent models • Hardening evaluation • Building multi-layered defenses with supervisor patterns

• Lead End-to-End MLOps and Productionization: Architect, implement, and maintain CI/CD pipelines for ML artifacts — including model evaluation, versioning, and automated deployment. Serve as the primary SME for operational excellence across the Invoca ML stack. • Design and Optimize SLM/LLM Deployment: Own the full inference infrastructure: model serving on Triton Inference Server, Baseten, and Kubernetes-based GPU infrastructure. Profile and tune for low latency and high throughput, and build robust, scalable APIs for internal and external model access. • Fine-Tune Language Models: Apply parameter-efficient fine-tuning methods (LoRA, QLoRA, PEFT) to adapt transformer-based SLMs and LLMs for high-impact NLP applications in conversation intelligence. • Evolve ML Infrastructure: Contribute to model training infrastructure, data pipelines, and data lake foundations to keep the systems powering our models reliable and scalable. • Collaborate Across Teams: Partner closely with Data Scientists, Data Engineers, and Applied AI Engineers to build the foundational ML systems behind Invoca's agentic AI products. • Deliver Customer Value: Work with product and engineering to understand customer needs and ship ML solutions that make a measurable difference.