Applied Ai/ml Lead

JPMorgan Chase JPMorgan Chase · Banking · Ciudad Autónoma de Buenos Aires, Argentina · Corporate Sector

Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions within Cloud Foundational Services. This role involves designing and executing enterprise-wide AI/ML frameworks, owning product analytics and measurement strategy, building predictive models, and developing tools for prompt-based agent evaluation and optimization. The position requires strong Python, SQL, and GenAI/RAG fundamentals, with a focus on building production-ready AI systems and providing technical leadership.

What you'd actually do

  1. Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions.
  2. Design and execute enterprise-wide, reusable AI/ML frameworks and core infrastructure to accelerate AI solution development.
  3. 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.
  4. Analyze user behavior and usage patterns to generate actionable insights quickly, translating ambiguous questions into structured analysis and recommendations that product management can operationalize.
  5. 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.

Skills

Required

  • Machine learning engineering
  • Classical ML and deep learning fundamentals
  • Text-based models (e.g. BERT, sentence-transformers)
  • Semantic search, classification, clustering
  • Pragmatic NLP techniques
  • Python for analytics and modeling (Pandas, scikit-learn)
  • Notebook-based workflows (Jupyter)
  • Visualization (seaborn, matplotlib)
  • System design
  • Application development
  • Testing
  • Operational stability
  • AI coding assistants (e.g. GitHub Copilot)
  • SQL
  • PL/SQL (Oracle preferred)
  • GenAI concepts
  • RAG fundamentals
  • Prompt engineering

Nice to have

  • NoSQL
  • Search/vector data technologies (vector databases, MongoDB, Elasticsearch)
  • QlikSense or comparable BI tools
  • API development (FastAPI, Spring Boot)
  • Modern development practices
  • Front-end development concepts
  • React

What the JD emphasized

  • 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.
  • 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 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.

Other signals

  • Deploying and scaling generative AI and agentic AI solutions
  • Building reusable AI/ML frameworks and core infrastructure
  • Defining and operationalizing product analytics and measurement strategy for AI products
  • Developing and maintaining tools for prompt-based agent evaluation, monitoring, and optimization