Are you ready to shape the future of banking with cutting-edge AI solutions? Join our dynamic team to transform client onboarding and document operations, partnering with business leaders to deliver measurable results in a fast-paced, collaborative environment.
Job Summary
As an AI Practitioner within the client onboarding and documentation organization, you will:
- Design and implement AI-driven solutions to streamline onboarding and document-intensive banking operations.
- Lead the development and deployment of document intelligence models, LLM-based data extraction frameworks, and automation pipelines to improve efficiency and accuracy.
- Serve as an individual contributor, deeply involved in solution design, model development, and deployment.
- Potentially transition into a team leadership role as the AI portfolio grows and solutions scale across functions.
- Apply advanced AI expertise and banking domain knowledge to deliver impactful, production-grade solutions for onboarding and KYC processes.
Job Responsibilities
- Deliver production-grade AI solutions for document understanding and onboarding automation.
- Build and deploy LLM-powered extraction and summarization frameworks for unstructured documents.
- Achieve measurable improvements in accuracy, turnaround time (TAT), and operational efficiency.
- Develop reusable AI modules and APIs to enable rapid adoption across operations functions.
- Lead and expand a specialized AI team as solution scale and business demand increase.
- Design, implement, and optimize AI models and algorithms for diverse business applications.
- Collaborate with stakeholders to gather requirements and translate them into effective AI solutions.
- Analyze large datasets to extract actionable insights and support data-driven decisions.
- Ensure all AI initiatives comply with data privacy, governance, and ethical standards.
- Monitor and maintain model performance, retraining as needed to ensure reliability.
- Drive innovation by exploring emerging AI frameworks and technologies for continuous improvement.
Required Qualifications, Capabilities, and Skills
- Atleast4 years of experience in AI/ML, with at least 4 years in NLP, Document AI, or Generative AI.
- Strong foundation in machine learning and deep learning algorithms (classification, clustering, entity extraction, semantic similarity).
- Hands-on experience with TensorFlow or PyTorch, scikit-learn, HuggingFace Transformers, and spaCy.
- Proven expertise in building document intelligence solutions (classification, OCR, key-value extraction, summarization).
- Proficiency in Python, SQL, Pandas, and experience with data ingestion and preprocessing pipelines.
- Experience deploying AI models to production, including lifecycle management and monitoring.
- Skilled in FastAPI or Flask, Docker, CI/CD, and API integration.
- Strong experience with LLM-driven workflows, prompt engineering, RAG, and LLM fine-tuning.
- Familiarity with OCR tools (Tesseract, Amazon Textract, Azure Form Recognizer, or Google Document AI).
- Demonstrated ability to drive AI solution design and deployment independently.
- Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
Preferred Qualifications, Capabilities, and Skills
- Experience in banking client onboarding, KYC/AML, and document verification processes.
- Familiarity with corporate onboarding documentation and regulatory compliance in BFSI or regulated domains.
- Experience with multimodal document models (LayoutLMv3, Donut, DocFormer) and advanced NER/entity linking.
- Integration experience with OpenAI, Anthropic, Cohere, or open-source LLMs (LLaMA, Mistral).
- Knowledge of data privacy, governance, and AI explainability frameworks in financial services.
- Track record of cross-functional collaboration with operations, data engineering, and low-code teams.
- Ph.D. or advanced specialization in AI or related field.