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 digitization through 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.

Base salary is determined based on the role, experience, skill set, and location.