Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As an Applied AI/ML Lead within Cloud Foundational Services in the Infrastructure Platforms (IP) Line of Business, you will be at the forefront of combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will be instrumental in building products that proactively surface knowledge and context to users, automate support workflows, and help teams resolve issues faster. You will define and maintain product value metrics, analyze user behavior, and build predictive models that directly inform product management decisions and roadmap prioritization.

Job responsibilities

• Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions.
  Design and execute enterprise-wide, reusable AI/ML frameworks and core infrastructure to accelerate AI solution development.
• Own the product analytics and measurement strategy by defining, operationalizing, and maintaining the metrics that demonstrate product value, adoption, and outcomes for stakeholders and leadership.
• Analyze user behavior and usage patterns to generate actionable insights quickly, translating ambiguous questions into structured analysis and recommendations that product management can operationalize.
• Build, validate, and iterate predictive models that support decision-making, including forecasting, propensity modeling, segmentation, and anomaly or trend detection, with outputs designed for product and engineering consumption.
• Apply context and prompt engineering techniques to improve prompt-based model performance.
• Develop and maintain tools and frameworks for prompt-based agent evaluation, monitoring, and optimization at enterprise scale.
• Build and maintain data pipelines and processing workflows for scalable, efficient data consumption.
• Contribute to the design and evolution of the reporting and data layer that measures product impact and surfaces insights to stakeholders.
• Write secure, high-quality production code and conduct code reviews.
• Partner with Engineering, Product, and Business teams to identify requirements and develop solutions.
• Communicate technical concepts and results to both technical and non-technical stakeholders, including senior leadership.
• Provide technical leadership, mentorship, and guidance to junior engineers, promoting a culture of excellence and continuous learning.

Required qualifications, capabilities, and skills

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent working experience).
• Experience in machine learning engineering, with a solid grounding in classical ML and deep learning fundamentals.
• Hands-on experience with text-based models, including transformer embeddings (e.g. BERT, sentence-transformers) for tasks such as semantic search, classification, and clustering, as well as pragmatic NLP techniques including regex-based and rule-based classifiers where appropriate.
• Strong proficiency in Python for analytics and modeling, including Pandas, scikit-learn, and notebook-based workflows (Jupyter), with strong capability in visualization using tools like seaborn and matplotlib.
• Hands-on experience in system design, application development, testing, and operational stability.
• Hands-on experience using AI coding assistants such as GitHub Copilot to accelerate development and improve productivity while maintaining code quality and controls.
• Strong SQL skills and deep experience with PL/SQL, with Oracle preferred; ability to work directly with relational datasets to build reliable, auditable metric logic and performant analytical queries.
• Working understanding of GenAI concepts and RAG fundamentals, with the ability to instrument, measure, and improve RAG-based application performance through quantitative evaluation and user-centric metrics.

Preferred qualifications, capabilities, and skills

• Familiarity with NoSQL and search/vector data technologies, including vector databases, MongoDB, and Elasticsearch, especially where relevant to GenAI retrieval and telemetry patterns.
• Experience with QlikSense or comparable BI tools used to publish, govern, and maintain dashboards for stakeholder consumption.
• Experience working closely with software engineering teams delivering production services, including API development with FastAPI (Python) and/or Spring Boot (Java), and familiarity with modern development practices that support stable analytics integration.
• Familiarity with front-end development concepts and collaboration patterns; React exposure is beneficial for partnering effectively on instrumentation, UX measurement, and dashboard embedding.
• Familiarity with NoSQL and search/vector data technologies, including vector databases, MongoDB, and Elasticsearch, especially where relevant to GenAI retrieval and telemetry patterns.
• Demonstrated ability to lead through influence as a senior individual contributor, setting standards for metric integrity, analytical rigor, and cross-team operating discipline within a new data sub-team.