JPMorgan Product Director - Data & Platform Solutions Engineering Lead 

United States, New York, New York 

747973348

As the Product Solutions Engineering Lead within the firm-wide CDAO Organization, you will build a team of Solutions Engineers to advise firm-wide teams in designing and integrating AI solutions within their application architecture.

Job responsibilities

• Lead and mentor a team of Solutions Engineer and data scientists, fostering innovation and collaboration.
• Provide technical expertise empowering our internal clients to design and deploy Machine Learning, Gen AI, and Agentic systems.
• Leverage your extensive ML experience to guide internal clients in their application development journey considering performance, evaluation, monitoring, resiliency and controls.
• Develop a deep understanding of firmwide standards for Model and Software Development Lifecycles and Information Security Controls.
• Anticipate risks associated with machine learning solutions and prediction/classification systems and strategize mitigation.
• Foster a transparent cross-functional partnership with product management, engineering and client engagement and influence peers and team members to uphold these standards.
• Lead presentations, whiteboard sessions, and technical workshops, effectively communicating the value, differentiators and capabilities of our solutions.
• Communicate complex issues clearly and credibly to senior management and stakeholders.

Required qualifications, capabilities, and skills

• 8+ years of experience or equivalent expertise delivering products, projects, or technology applications.
• Proficiency in programming languages such as Python, R, or Java, with a strong emphasis on Python.
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, and industry leading GenAI models.
• Strong skills in data manipulation and analysis using tools like Pandas, NumPy, and SQL.
• Experience in developing, training, and deploying machine learning models, Gen AI models and including knowledge of model evaluation and optimization techniques.
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
• Experience with big data technologies like Hadoop, Spark, or Apache Kafka for handling large datasets.
• Proficiency in using version control systems like Git.
• Experience in building and consuming APIs and working with microservices architecture.
• Understanding DevOps and MLOps practices for automating and streamlining the machine learning lifecycle.
• Strong foundation in mathematics and statistics, including knowledge of linear algebra, calculus, probability, and statistical methods.