• Build and maintain automated workflows using tools like n8n, Make, or Zapier. • Use AI coding tools (Claude Code, Cursor) to prototype and ship internal tools. • Identify repetitive processes across teams and design automation solutions. • Integrate AI capabilities (LLMs, APIs) into workflows to enhance productivity. • Document automations and train team members on how to use them. • Troubleshoot and improve existing workflows as needs evolve. • Collaborate with engineering, ops, and product to prioritize high-impact automations.

• Design, build, and deploy AI agents to support internal and client projects. • Automate workflows in monday.com and integrate with other SaaS tools such as Microsoft 365, Slack, and similar platforms. • Identify opportunities to streamline and optimize processes using AI and automation. • Collaborate with cross-functional teams to understand operational needs and deliver solutions. • Document processes, provide training, and ensure adoption of AI solutions and workflows. • Automate core project management functions, including scheduling, project cost estimating, budget tracking, change order management, and resource allocation. • Streamline document control, RFI/submittal tracking, meeting minutes, and project reporting through AI-driven workflows.

• Design and build scalable and efficient ML models using both classic and generative AI techniques. • Serve as the go-to expert for all machine learning-related inquiries, providing guidance and mentorship to other team members. • Advise and guide teams on how to deploy sophisticated and scalable ML models and pipelines on Google Cloud Platform, leveraging services such as Vertex AI, BigQuery ML, and AI Platform. • Conduct in-depth research to stay at the forefront of the rapidly evolving fields of machine learning and artificial intelligence. • Collaborate with cross-functional teams to identify opportunities for leveraging machine learning to drive innovation and solve critical business and operational problems. • Ensure the robustness, fairness, and ethical implications of all developed models. • Effectively communicate complex technical concepts to both technical and non-technical stakeholders.

• Design and curate evaluation datasets for retrieval quality • Measure retrieval quality using metrics like Recall@k, Precision@k, MRR, and NDCG@k • Recommend data cleaning/normalization strategies • Evaluate and optimize chunking strategies • Assess embedding and re-ranking strategies • Evaluate generation quality in context • Attribute failures across the pipeline • Conduct systematic error analysis on AI/ML system outputs • Design and validate LLM-as-Judge evaluators • Build and maintain golden datasets for CI regression testing of AI pipelines • Lead or facilitate annotation workflows