Are you passionate about leveraging advanced technology to solve complex business challenges? As an applied AI/ML, you will have the opportunity to shape the future of document management through cutting-edge AI and machine learning. Join a collaborative team where your expertise will drive impactful solutions and strategic outcomes. This role offers a platform to innovate, lead, and make a difference across the organization.
As a Machine Learning Engineer – Document Digitization (LLMs)-Vice President in our organization, you will design, develop, and deploy secure, scalable, and innovative technology products that transform how documents are processed and managed. You will use advanced AI and machine learning to extract, analyze, and manage information, driving strategic business outcomes. You will collaborate with cross-functional teams, mentor others, and continuously seek opportunities for improvement and innovation.
Job 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.
Required qualifications, capabilities, and skills
- 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 (rather than research-heavy model development).
- 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, capabilities, and skills
- 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 (nice to have).
- Experience leading design/code reviews and mentoring teams.