AI Engineer, Agentic AI, RAG & Document Intelligence

Company Overview: Business Integration Partners (BIP) is Europe’s fastest growing digital consulting company and is on track to reach the Top 20 globally by 2030. With a growing presence across the United States—including New York, Charlotte, Chicago, and Houston—BIP operates at the intersection of business strategy, technology, and data to deliver impactful solutions for clients. With more than 6,000 professionals worldwide, BIP partners with organizations across Financial Services, Insurance, Payments, and other industries. Our capabilities span Data & AI, Cybersecurity, Risk & Compliance, Digital Transformation, and Change Management. We combine deep industry expertise with engineering excellence to help clients modernize platforms, unlock data insights, and drive innovation. As part of our continued expansion in the United States, BIP is strengthening its Capital Markets technology practice. Our teams work closely with trading, risk, and front-office technology organizations to modernize critical platforms, enhance analytics capabilities, and support complex financial product ecosystems. About the Role: BIP is hiring an AI Engineer / Applied AI Scientist to support a high-profile technology transformation program at a leading global investment bank, reporting directly into the CIO organization. This initiative will design, build, and deploy production-grade AI systems leveraging modern agentic architectures, retrieval-augmented generation (RAG), document intelligence, and cloud-native ML platforms. You will help bridge the gap between cutting-edge AI techniques and scalable enterprise solutions. Building systems that enable information extraction, knowledge discovery, explainability, and intelligent automation within highly regulated environments. This is a hands-on role for someone who enjoys moving across research, prototyping, and production delivery. Key Responsibilities: Agentic AI & RAG • Design and develop production AI services using modern agentic frameworks and LLM ecosystems. • Build intelligent workflows for search, retrieval, summarization, knowledge discovery, and conversational experiences. • Implement scalable multi-agent and retrieval pipelines. Document Intelligence • Build document ingestion, OCR, parsing, and information extraction systems for complex enterprise datasets. • Develop workflows for structure analysis, table recognition, and metadata extraction. • Improve extraction quality and automation performance across diverse document types. Machine Learning & NLP • Fine-tune and deploy models for summarization, entity extraction, classification, and knowledge generation. • Develop explainable ML workflows and anomaly detection systems. • Evaluate model quality and continuously improve performance. Data & Platform Engineering • Build Python-based ML and data pipelines using Databricks, Spark, and MLflow. • Support model deployment, experiment tracking, and monitoring. • Contribute to scalable cloud-based architectures. Collaboration • Partner with quantitative teams, risk stakeholders, infrastructure teams, and business users. • Help ensure AI systems are secure, compliant, reliable, and production ready. Required Skills & Experience: • 4+ years of experience in AI Engineering, Data Science, Machine Learning, or related technical roles. • Strong Python development experience. • Hands-on experience building and deploying AI/ML systems in enterprise environments. • Experience with LLM ecosystems and agentic frameworks such as: • LangChain • LangGraph • OpenAI • Claude • similar frameworks • Experience with one or more: • PyTorch • TensorFlow • Hugging Face • Experience with • Spark • Databricks • MLflow • cloud environments (Azure, AWS, or GCP) • Experience building APIs or production services using FastAPI, Flask, or similar technologies. Highly Preferred: • Experience with document intelligence or information extraction systems. • Experience working with financial, legal, regulatory, or enterprise document workflows. • Familiarity with explainability tools (SHAP or similar). • Experience implementing RAG systems or knowledge platforms. • Demonstrated innovation through production delivery, published research, open-source contributions, patents, or hackathons. • Experience within fin