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Machine Learning Engineer - Document Digitization (LLMs)-Vice President

JPMorganChase Jersey City, NJ

Jersey City, NJ

Today

$164k - $260k/yr Full time Ai Ml Amazon Eks Artificial Intelligence

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Job Description

Join JPMorgan Chase in Jersey City, onsite, as a Senior Vice President level Machine Learning Engineer focused on designing, developing, and deploying AI powered document digitization solutions that leverage large language models. You will lead cross functional collaboration, mentor engineers, and drive scalable AI/ML platforms with governance in mind. This role offers a competitive compensation package including a base salary in the USD 164,350 to 260,000 per year range, as well as equity-based incentives and a comprehensive benefits program.

Benefits

Equity/stock-based compensation
Competitive base salary
Commission-based pay and/or discretionary incentive compensation
Comprehensive health care coverage
On-site health and wellness centers
Retirement savings plan
Backup childcare
Tuition reimbursement
Mental health support
Financial coaching

Responsibilities

Lead the design, development, and integration of AI powered document digitization solutions, focusing on extracting information and insights from diverse document types.
Oversee the full AI/ML lifecycle from training and validation to deployment, monitoring, and ongoing production improvements.
Utilize generative AI and large language models to automate and optimize document workflows.
Build and maintain scalable digitization pipelines using Python, AI frameworks, and cloud technologies.
Provision and manage cloud resources with infrastructure as code tools such as Terraform and AWS services including SageMaker and Bedrock.
Ensure scalability, reliability, security, and compliance of AI/ML solutions, following best practices and governance standards.
Collaborate with cross functional teams to reimagine legacy document processing systems using generative AI and LLMs.
Develop and maintain dashboards and reporting tools to monitor digitization accuracy, workflow efficiency, and business impact.
Mentor junior engineers and promote best practices in AI/ML, software engineering, and testing.
Conduct model validation, human in the loop reviews, and implement continuous improvement strategies for digitization accuracy.
Contribute to communities of practice and explore new and emerging technologies.
Technologies you will work with: Python, Terraform, AWS SageMaker, AWS Bedrock, Docker, Kubernetes, Amazon EKS, Java, Spring Boot, React.js, AngularJS, TensorFlow, PyTorch, Scikit-learn, PyTorch Lightning, MongoDB Atlas, Elasticsearch/OpenSearch, Neo4j, LLMs.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field, with relevant industry experience.
Strong Python proficiency for building production-grade AI services and data/document pipelines.
Solid Java experience, including API and microservice development with Spring Boot; familiarity with front-end tech (React.js, AngularJS) is a plus.
Hands-on experience delivering LLM powered or GenAI applications in production, including evaluation, observability, guardrails, and continuous improvement for use cases like document understanding and workflow automation.
Experience with MLOps / LLMOps practices in production environments (CI/CD, automated testing, deployment strategies, monitoring, incident response).
Working knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, PyTorch Lightning) with emphasis on integrating models and services into scalable systems.
Experience with AWS and cloud-native delivery, including SageMaker and/or Bedrock, containerization (Docker, Kubernetes, Amazon EKS), and infrastructure as code (Terraform).
Familiarity with NoSQL / search / graph technologies (MongoDB Atlas, Elasticsearch/OpenSearch, Neo4j) for document search and knowledge retrieval.
Experience with agentic coding approaches, autonomous/assisted code agents, orchestration patterns, and tool-use frameworks to accelerate document digitization workflows.
Strong understanding of SDLC, CI/CD, resiliency, and security practices; proven problem solving and collaborative communication skills.
Proven ability to drive development with AI technologies while maintaining rigorous engineering standards, including testing, code quality, and governance.

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