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

About the role

Join a collaborative team where your expertise in applied AI/ML will drive impactful solutions and strategic outcomes in document management. This role offers a platform to innovate, lead, and shape the future of document processing using cutting-edge AI and machine learning technologies.

Responsibilities

• Lead the design, development, and integration of AI-powered document digitization solutions, focusing on extracting information and insights from diverse document types.
• Manage the end-to-end AI/ML lifecycle: model training, validation, deployment, monitoring, and continuous improvement in production environments.
• Employ generative AI and large language models (LLMs) to automate and optimize document workflows.
• Build and maintain scalable document digitization pipelines using Python, AI frameworks, and cloud technologies.
• Provision and manage cloud resources using infrastructure as code tools (Terraform) and AWS services (SageMaker, Bedrock).
• Ensure scalability, reliability, security, and compliance of AI/ML solutions, adhering to 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 review, 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 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 building APIs and microservices with Spring Boot; familiarity with front-end technologies (React.js, AngularJS) is a plus.
• Hands-on experience delivering LLM-powered/GenAI applications in production (e.g., 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) with primary 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 (Mongo 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 delivery of document 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).

Preferred Qualifications

• Experience in financial services, especially investment banking or credit risk operations.
• Expertise in agentic AI frameworks, prompt optimization, evaluation harnesses, and fine-tuning/parameter-efficient tuning of smaller language models (SLMs) where appropriate.
• Familiarity with distributed computing, data sharing, and DDP training.
• Experience leading design/code reviews and mentoring teams.

Benefits

• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.
• Discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.

Pay

Competitive total rewards package including base salary determined based on the role, experience, skill set, and location. Additional details about total compensation and benefits will be provided during the hiring process.

About the team

Our professionals in Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. These teams ensure we're setting our businesses, clients, customers, and employees up for success.