Applied AI/ML Lead

Next Frontier Capital; Tampa (FL); On-site; USD 180,000 - 280,000; Full time

Job summary

Next Frontier Capital is seeking an Applied AI/ML Lead to design, develop, and deploy AI/ML solutions focused on image classification, text categorization, and data extraction from scanned TIF documents. You will architect CV pipelines, leverage CRNNs, and integrate OCR with multimodal document understanding.

You will lead model development, optimize pipelines on AWS SageMaker, and explore Bedrock capabilities to augment traditional extraction.

Qualifications

• Bachelor’s degree or MS or PhD in a quantitative discipline (e.g., CS, Math, OR, Data Science).
• 7+ years in applied ML/AI with 2+ years leading teams or large-scale ML initiatives.
• Proficiency in Python and enterprise languages with PyTorch, TensorFlow, Hugging Face Transformers, OpenCV and Pillow for model development and image processing; Java/Groovy for backend integration; Oracle for data retrieval.
• Deep expertise in CV and NLP models with hands-on CRNNs for image classification and feature extraction; multimodal understanding.
• Experience with OCR tech and image preprocessing for TIFFs; OCR accuracy optimization and post-processing.
• Hands-on AWS SageMaker and Amazon Bedrock end-to-end ML workflows; model deployment on EKS with Docker; GPU optimization.
• Strong MLOps knowledge: MLflow, SageMaker Pipelines; ability to manage experiments and lifecycle.

Responsibilities

• Lead the design, development, and deployment of AI/ML solutions for image classification, text categorization, and data extraction from scanned TIFs.
• Develop models for document understanding, text categorization, and NLP tasks; integrate OCR-extracted text with layout information.
• Integrate OCR and generative AI into the document processing pipeline using Bedrock for document understanding and classification.
• Architect scalable ML training and inference pipelines on AWS SageMaker; deploy microservices on AWS EKS with low latency inference.
• Establish MLOps practices, including model versioning, retraining triggers, drift detection, and dashboards.
• Build and lead a team of ML engineers and applied scientists to drive experimentation and KPI-aligned evaluation.

Skills

Python

PyTorch

TensorFlow

Hugging Face Transformers

OpenCV

Pillow

Java

Groovy

Oracle databases

CRNN architectures

multimodal document understanding

transformer NLP models

OCR technologies

AWS SageMaker

Amazon Bedrock

Docker

Kubernetes

GPU inference

MLOps tools

MLflow

SageMaker Pipelines

communication skills

Education

Bachelor’s degree or MS or PhD in quantitative discipline

Tools

Docker

Kubernetes

AWS SageMaker

Amazon Bedrock

Oracle databases

Job description

As Applied AI/ML Lead within Commercial & Investment Bank with the Healthcare Provider team, you will lead the design, development, and production deployment of AI/ML solutions focused on image classification, text categorization, and data extraction from scanned TIF documents. You will architect and implement computer vision pipelines leveraging CRNN architectures for document type identification, page-level categorization, and visual feature extraction.

Responsibilities

• Lead the design, development, and production deployment of AI/ML solutions focused on image classification, text categorization, and data extraction from scanned TIF documents and evaluate and explore additional models and architectures to continuously improve classification accuracy, extraction quality, and processing efficiency.
• Drive the development and fine‑tuning of models for document understanding, text categorization, named entity recognition, and semantic understanding and combine visual layout information, textual content, and spatial relationships to extract structured data from complex scanned documents, while enabling automated categorization and metadata tagging of OCR‑extracted text.
• Lead the integration and optimization of OCR technology and generative AI capabilities into the document processing pipeline, ensuring high‑accuracy text extraction from scanned TIF images across diverse document types, layouts, fonts, and quality levels. Leverage Amazon Bedrock to explore foundation model capabilities for intelligent document understanding, classification, document summarization, and augmenting traditional extraction pipelines.
• Architect and implement scalable ML training and inference pipelines using AWS SageMaker, managing model training, hyperparameter tuning, distributed training for large vision models, and real‑time/batch inference endpoint deployment. Collaborate with software engineering teams to integrate trained models into Java/Python‑based microservices deployed on AWS EKS, ensuring low‑latency, high‑throughput inference for production document processing workloads.
• Establish robust MLOps practices and annotation workflows, including model versioning, automated retraining triggers, A/B testing of model variants, drift detection on document distributions, and comprehensive performance monitoring dashboards and design and manage labeling strategies for training data, ensuring high‑quality ground truth datasets for image classification, text categorization, and document extraction tasks.
• Build and manage a team of ML engineers and applied scientists, fostering a culture of experimentation, rapid prototyping, and rigorous evaluation of model performance against business KPIs.

Required qualifications, capabilities, and skills

• Bachelor’s degree or MS or PhD in quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science.
• 7+ years of experience in applied ML/AI roles with at least 2+ years leading teams or large‑scale ML initiatives.
• Advanced proficiency in Python and enterprise languages, with deep experience in PyTorch, TensorFlow, Hugging Face Transformers, OpenCV, and Pillow for model development and image processing. Proficiency in Java and/or Groovy for integrating ML capabilities into backend services and enterprise application ecosystems. Familiarity with Oracle databases for feature extraction, training data retrieval, and integration with ML workflows.
• Deep expertise in computer vision and NLP models, with hands‑on experience implementing and fine‑tuning CRNN‑based architectures for image classification and feature extraction. Strong experience with multimodal document understanding combining text, layout, and image features. Proficiency in transformer‑based NLP models for text categorization, sequence labeling, named entity recognition, and semantic analysis of OCR‑extracted content.
• Practical experience with OCR technologies and image preprocessing, for text extraction from scanned documents, with an understanding of OCR accuracy optimization, preprocessing techniques, and post‑processing correction. Experience with image preprocessing for scanned documents in TIF format, including multi‑page handling, resolution normalization, deskewing, binarization, and noise removal.
• Deep hands‑on experience with AWS SageMaker and Amazon Bedrock, including end‑to‑end ML workflows such as training jobs, processing pipelines, model registry, distributed training, and real‑time/batch inference endpoints. Practical experience leveraging foundation models, prompt engineering, and building generative AI‑augmented document processing solutions. Experience deploying and scaling ML models as containerized microservices on AWS EKS using Docker and Kubernetes, with expertise in optimizing GPU‑based inference workloads.
• Strong knowledge of MLOps tools and practices, including MLflow, SageMaker Pipelines, or equivalent platforms for experiment tracking, pipeline automation, and model lifecycle management. Excellent leadership and communication skills with the ability to present complex technical concepts to senior leadership and non‑technical audiences.

Preferred qualifications, capabilities, and skills

• Domain expertise in the healthcare industry.
• Experience in applied ML/AI roles in document processing, computer vision, or NLP domains.

Equal Opportunity Employment

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans.