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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

Senior Vice President level Machine Learning Engineer at JPMorgan Chase, focusing on designing, building, and deploying AI powered document digitization solutions with LLMs in Jersey City, NJ (onsite).

Responsibilities

Lead the design, development, and integration of AI powered document digitization solutions to extract information from diverse document types.
Oversee the end-to-end AI/ML lifecycle: training, validation, deployment, monitoring, and production grade continuous improvement.
Apply 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 (Terraform) and AWS services such as SageMaker and Bedrock.
Ensure scalability, reliability, security, and governance compliance of AI/ML solutions.
Collaborate with cross functional teams to reimagine legacy document processing using generative AI and LLMs.
Develop 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.

Requirements

Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or a related field with relevant industry experience.
Strong proficiency in Python for building production grade AI services and data/document pipelines.
Strong working proficiency in Java, including API and microservice development with Spring Boot; familiarity with front end technologies (React.js, AngularJS) is a plus.
Hands-on experience delivering LLM powered or GenAI applications in production (document understanding, retrieval augmented generation, workflow automation) including evaluation, observability, guardrails, and continuous improvement.
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) focused 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) and their use in document search and knowledge retrieval.
Experience with agentic coding approaches, autonomous/assisted code agents, orchestration patterns, and tool-use/agent frameworks to accelerate digitization workflows.
Strong understanding of SDLC, CI/CD, resiliency, and security practices; proven problem solving, communication, and collaboration skills.
Demonstrated ability to accelerate development using AI technologies while maintaining engineering rigor (testing, code quality, governance).

Technologies

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

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

About JPMorganChase

JPMorganChase is a long standing financial services institution serving consumers, small businesses, and large organizations worldwide under the J.P. Morgan and Chase brands. With a history spanning more than 200 years, the firm leads in investment banking, consumer and small business banking, commercial banking, payments processing, and asset management.

About the Team

Corporate Functions teams cover a broad range of areas from finance and risk to human resources and marketing. These professional groups play a key role in enabling business success for clients, customers, and employees across the organization.

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13d ago

Senior Data Scientist

Neptune Technology Group Inc. Duluth, GA

Duluth, GA

13d ago

Full time Senior Amazon Web Services Analytics

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Job Description

Neptune Technology Group Inc. is seeking a Senior Data Scientist to design and implement machine learning models and data driven solutions that enhance Neptune's water utility intelligence platform. This role involves working with large-scale IoT data from millions of water meters, developing predictive analytics capabilities, and deploying AI solutions into production. You will collaborate with Product Management and Engineering teams, mentor junior data scientists, and help drive Neptune's AI transformation. This on-site position is based in Duluth, GA.

Responsibilities

Communicate decisions, designs, and outcomes to stakeholders at all levels across the organization.
Collaborate with cross-functional teams to deliver high-quality machine learning models and data science solutions.
Understand and refine requirements defined by Product Management for AI powered features.
Design and implement machine learning models for water consumption forecasting, anomaly detection, leak detection, and predictive maintenance.
Develop and deploy production ready machine learning pipelines on cloud infrastructure (AWS).
Analyze large-scale time-series data from IoT devices and water utility operations.
Build and optimize data processing workflows using PySpark and distributed computing frameworks.
Create data visualizations and analytics dashboards to communicate insights to stakeholders.
Conduct exploratory data analysis to identify patterns, trends, and opportunities in metering data.
Perform feature engineering and model selection to optimize predictive performance.
Evaluate model performance and implement monitoring solutions for production ML systems.
Collaborate with software engineers to integrate ML models into the Neptune 360 platform.
Provide technical guidance to Product Management on data science capabilities and feasibility.
Document data science methodologies, model architectures, and analytical findings.
Stay current with the latest developments in machine learning, AI, and data science best practices.
Mentor junior data scientists and disseminate technical knowledge within the organization.
Review code and model implementations of other team members.
Participate in sprint planning and demonstrate completed work at the end of each iteration.
Work with Python, SQL, PySpark, AWS services (SageMaker, Bedrock, Lambda, Redshift), and ML frameworks.
Contribute to Neptune's AI strategy and identify new opportunities for data-driven innovation.

Requirements

5+ years of experience in data science, machine learning, or related analytical roles.
5+ years of experience with Python and data science libraries (pandas, NumPy, scikit-learn, TensorFlow/PyTorch).
Strong experience with SQL and working with large-scale databases (Redshift, PostgreSQL, MySQL).
Experience with PySpark and distributed computing frameworks for large-scale data processing, including working with common data formats such as JSON and Parquet.
Proven track record of deploying machine learning models to production environments.
Experience with cloud platforms, preferably AWS (SageMaker, Bedrock, Lambda, S3, Redshift).
Experience with time-series analysis and forecasting methods.
Understanding of MLOps practices and model lifecycle management.
Experience building RESTful APIs for model serving.
Strong statistical analysis and experimental design skills.
Experience with data visualization tools and techniques.
Experience working in Agile/iterative development environments.
Ability to communicate complex technical concepts to non-technical stakeholders.
Experience with version control systems (Git) and CI/CD pipelines.
Continued professional self-improvement through courses, certifications, or research.
Preferred: Experience with AWS big data services (Glue, EMR, Athena).
Preferred: Experience with IoT data, utility operations, or water management systems.
Preferred: Experience with generative AI and large language models.

Technologies

Python
SQL
PySpark
AWS
SageMaker
Bedrock
Lambda
Redshift
S3
PostgreSQL
MySQL
JSON
Parquet
Pandas
NumPy
scikit-learn
TensorFlow
PyTorch
RESTful APIs
Git
CI/CD pipelines
Glue
EMR
Athena

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1mo ago

Data Engineer II, AAE

Amazon Development Center U.S., Inc. Bellevue, WA

Bellevue, WA

1mo ago Closed

$132k - $179k/yr Full time Analytics Apache Airflow Artificial Intelligence

This position is no longer accepting applications (closed Jul 3, 2026).

Job Description

Based in Bellevue, WA onsite, this Data Engineer II role focuses on building end-to-end data platforms for AWS AI services and delivering executive insights through automated analytics and revenue reporting.

Responsibilities

Architect and implement complete data platforms for new AWS AI services, establishing schemas, data models, ETL/ELT pipelines, and analytics infrastructure where none exists today
Develop and maintain production ETL/ELT pipelines with AWS Glue, Airflow, Spark, and Python to ingest data from operational, commercial, and telemetry sources into unified data models
Create agentic data workflows featuring automated reporting pipelines that leverage AI/ML to generate business insights, weekly business review summaries, and anomaly detection with minimal manual intervention
Build event-driven data architectures using CDK, Lambda, SNS/SQS, and S3 event notifications to support real-time data ingestion and processing
Develop executive dashboards and self-serve analytics using QuickSight for VP/GM-level leadership across multiple service lines
Ensure revenue data accuracy by implementing and validating revenue attribution models, discount calculations, and financial data pipelines that feed CFO-mandated reporting
Design data models that support both operational analytics (feature adoption, customer health, churn signals) and financial reporting (revenue, billing, forecasting)
Collaborate with Product Management, Finance, Service Engineering, GTM, and Data Science teams to translate business questions into scalable data solutions
Optimize pipeline performance by reducing runtimes, eliminating redundant processing, and improving SLA compliance across production workloads
Mentor engineers, contribute to team standards, and foster a culture of automation, code quality, and operational excellence

Requirements

5+ years of data engineering experience
3+ years of developing and operating large-scale BI data structures for analytics using ETL/ELT processes
3+ years of developing and operating large-scale BI data structures for analytics using data modeling
Experience with data modeling, data warehousing, and building ETL pipelines

Technologies

AWS Glue
Redshift
Athena
QuickSight
Bedrock
SageMaker
CDK
Lambda
SNS
SQS
S3
Airflow
Spark
Python
Kendra
Kiro

Benefits

Health insurance (medical, dental, vision, prescription, Basic Life & AD&D, option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
401(k) matching
Paid time off
Parental leave
Sign-on payments
Restricted stock units (RSUs)

A day in the life

Design data models for newly launched AWS AI services and deploy ETL pipelines to onboard telemetry and revenue data
Validate data accuracy across financial reporting systems
Architect CDK-based event-driven pipelines and collaborate with Product Managers to define launch metrics
Resolve data discrepancies surfaced by Finance and optimize production queries feeding VP-level weekly business reviews

About the team

The AI Services Data Engineering team builds the data infrastructure behind AWS Armored AI products including Bedrock, AgentCore, QuickSight, Q Business, Kendra, Kiro, and Transform. Our data powers metrics and reporting that inform executive visibility into Agentic AI revenue, adoption, and growth, including automated weekly business reviews with agent-generated summaries and revenue attribution models for multi-billion dollar pricing programs and launch telemetry.

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Aws Sagemaker Data Jobs

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

Senior Data Scientist

Data Engineer II, AAE

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