• 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.

• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams

• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams