Summary
Lead technical design and implementation of conversational analytics solutions within the Enterprise Data Platform, driving strategy for conversational AI and natural language processing to extract actionable insights and improve customer experience. Serve as a technical leader collaborating with cross functional teams to build scalable, reliable, and cost effective analytics capabilities.
Responsibilities
- Architect conversational analytics solutions that process and analyze large datasets in the data platform and BI tools.
- Drive technical vision for conversational AI and NLP capabilities across the enterprise.
- Establish MLOps best practices for model deployment, monitoring, and operational excellence.
- Design and implement data pipelines to ingest and process conversational data from multiple sources.
- Mentor and develop senior engineers, providing technical guidance and leadership.
- Collaborate with data science, product, customer service, and business stakeholders to deliver solutions.
- Ensure data quality, governance, security, privacy, and compliance for conversational data.
- Lead architectural discussions and make key technical decisions for conversational analytics.
- Partner with platform teams to integrate analytics capabilities into the Enterprise Data Platform.
Requirements
- 8+ years of relevant software engineering experience focused on data engineering, machine learning, or AI systems.
- 3+ years of technical leadership experience leading engineering teams.
- Bachelor's degree in Computer Science Data Science or related field or equivalent experience; master's preferred.
- Deep expertise in conversational AI NLP and experience with large language models and intent detection.
- Strong ML engineering background including model training deployment monitoring and MLOps.
- Experience with cloud platforms preferably Google Cloud Platform and big data technologies.
- Experience with streaming data architectures and event driven systems for unstructured text.
- Proficient programming skills in Python SQL and familiarity with ML frameworks and libraries.
- Experience with data governance privacy and security for customer conversation data.
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