• 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.

• You will bring statistical rigor to business decisions. • As part of the BI team, your mission is to go beyond descriptive analysis by applying statistical and machine learning models to find patterns, validate hypotheses, and generate forecasts that guide real strategies. • Model complex problems using Python. • Work with large volumes of data in ClickHouse and BigQuery. • Collaborate with product and operations teams to ensure models deliver real value. • Conduct statistical analyses to answer business questions: hypothesis testing, correlation analysis, segmentation, and predictive modeling. • Perform exploratory data analysis (EDA) to understand the data before modeling, identifying patterns and inconsistencies. • Write complex SQL queries in ClickHouse and BigQuery to extract and prepare data for analysis and modeling. • Use Python for data manipulation, exploratory analysis, and model prototyping. • Communicate results and insights clearly to business teams — translating statistics into decisions. • Collaborate with the BI team to define metrics, ensure data quality, and establish analytical best practices. • Monitor models in production, tracking performance and proposing continuous improvements.