• Develop and integrate ML, NLP, and Generative AI models • Build and optimize agentic workflows using LangChain, LangGraph, or similar frameworks • Develop LLM-powered solutions using modern Generative AI technologies • Prototype internal tools and interfaces with Streamlit or Gradio • Collaborate with engineering and product teams to deliver AI-driven features • Manage development and runtime environments with Docker • Maintain clean and scalable code using Git-based workflows • Query and analyze structured data using SQL • Contribute to model deployment and operations in a cloud environment (primarily Azure)

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies

• Own the research-to-production pipeline • Partner directly with research scientists to productionize new models • Build and extend the tooling and abstractions • Design and own model release gates • Optimize models and serving for production • Strengthen the build and delivery layer for models • Establish benchmarking and validation • Build the feedback loop

• Build and iterate on consumer-facing AI features powered by large language models (LLMs) and related generative AI systems • Collaborate with engineers across the AI engineering stack — prompt engineering, agentic workflow optimization, evaluation and more • Run structured experiments (e.g., A/B tests, offline evaluations) to measure impact on engagement, quality, and trust metrics • Partner closely with product and engineering to translate user needs into scalable AI-powered solutions • Strengthen model reliability through monitoring, guardrails, quality analysis, and failure mode investigation • Optimize the latency, cost, and scalability of production AI systems