Senior machine learning engineer

OVERVIEW OF 73 STRINGS: 73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit. As of today, 73 Strings has developed a unique AI-augmented platform and solutions that aim to assist experts in their day-to-day operations: 73 Extract: transforms unstructured data into structured, actionable information. This breakthrough allows for a fresh perspective on your data, enabling more informed and strategic investment decisions. 73 Monitor: automates structured data collection, offers enhanced visualization capabilities, and seamlessly gathers portfolio company financial data in multiple languages. 73 Value: digitalizing valuations in equity and credit and control your valuation insights with AI, advanced analytics and tailored workflows delivering faster, more accurate, and audit ready valuations. Clients of 73 Strings hail from Europe, the US and the Middle East, and include alternative investment funds, fund administrators and management companies, as well as corporates, banks, advisors, and other finance professionals. Over the last year, 73 Strings has added clients with a combined AUM of $9 trillion. In June 2023, 73 Strings completed a series A funding that was led by Blackstone Innovations Investments, Blackstone's early-stage strategic investment arm, and Fidelity International Strategic Ventures (FISV), with participation from Broadhaven Ventures. This demonstrates prime confidence in 73 Strings’ innovative technology and market potential. The company has offices in Paris, London, New York, Toronto, London, Riyadh, São Paulo and Bengaluru. THE TEAM: The current leadership of 73 Strings has extensive professional expertise in the valuation of private assets and understand the importance of informed decision-making when it comes to managing capital: Yann Magnan, Co-Founder and CEO : Previous experience at E&Y (Valuation Advisory) and Duff & Phelps, most recently as head of EMEA and member of the Global Operating Committee. Abhishek Pandey, Co-Founder and Deputy CEO : Previous experience at Corporate Finance (Deloitte, Grant Thornton) and Duff & Phelps most recently as Managing Director in India. Vinod Vijapur, Co-Founder and CTO : Previous experience in banking at HDFC Bank, Royal Bank of Scotland and at Bo AML, most recently as Head of APAC Regional Applications – Program Management. Sambeet Parija, Co-Founder and CPO : Previous experience at Duff & Phelps as part of the valuation team in India having led several strategic projects across different sectors. Jazmin Hogan, North America Lead : Previous experience at the Blackstone, Apollo and Kohlberg & Co, most recently as Director of Valuations and Analytics. Celine Bes, EMEA Lead , Previous experience in Investment Banking and Valuation at Goldman Sachs and Private Equity and Transaction Advisory at E&Y. The team is supported by a global network of advisors formed by highly seasoned veterans with combined 300+ years of financial advisory and private markets experience. ROLE AND BACKGROUND: As a Machine Learning Engineer, you'll be at the forefront of transforming document intelligence through AI innovation. You'll develop sophisticated models that power information extraction, document understanding, and intelligent search capabilities. Working at the intersection of natural language processing and structured data, you'll build systems that can automatically comprehend, analyze, and derive insights from complex documents and databases. Your expertise in creating scalable ML solutions will directly impact our ability to unlock valuable insights from vast amounts of unstructured data. Core Competencies: 1. Programming & Development Advanced Python programming Experience with ML frameworks (Py Torch, Tensor Flow) API development and integration Version control (Git) and CI/CD practices 2. Computer Vision Document layout analysis OCR and text extraction Image preprocessing and enhancement Table structure recognition Document classification 3. Natural Language Processing Large Language Models (LLMs) Text classification and named entity recognition Information extraction and summarization Question-answering systems Semantic search implementation Vector databases and embeddings 4. Data Engineering & MLOps Database management (SQL/No SQL) Data pipeline development Model deployment and monitoring Cloud platforms (AWS/Azure/GCP) Responsibilities: Create, build, and implement state-of-the-art models for various purposes, including information extraction—extracting information from extensive data sets presented in diverse formats (such as tables, text, and charts), whether structured or unstructured. Work closely with data scientists, data engineers, software engineers and domain experts to establish project parameters, analyze data, and seamlessly incorporate models into production settings. Execute and document thorough experiments to assess and enhance model effectiveness. Write automated tests for ML pipelines to ensure production robustness. Supervise and uphold deployed models, guaranteeing optimal functionality and addressing any issues with stakeholders proactively. Remain at the forefront of AI advancements, actively participating in sharing knowledge and fostering innovation within the team. Incorporate model governance and regulatory considerations in model deployment, especially when dealing with financial data. Requirements: Completion of a Master’s degree in Computer Science, Data Science, or a related field (OR equivalent practical experience). Strong understanding of software engineering principles, including design patterns, code optimization, and testing best practices (unit testing, integration testing). Demonstrated proficiency in the Python programming language. Previous experience with code-versioning tools, specifically git. Familiarity with MLOps practices, including setting up CI/CD pipelines for ML models, automating workflows, and monitoring production models. Experience deploying models on cloud platforms (e.g., Azure, AWS, Google Cloud) and using containerization technologies like Docker and Kubernetes. Preferred experience with data engineering tools such as Apache Spark, Airflow, or Databricks. Knowledge of building interactive visualizations with tools like Streamlit or Tableau. Understanding of data privacy, model governance, and regulatory considerations when dealing with financial data. Robust understanding of Machine and Deep Learning principles. In-depth knowledge of Natural Language Processing (NLP) fundamentals, covering regular expressions, text pre-processing, tokenization, stemming, lemmatization, and language models. Practical exposure to fundamental concepts of Computer Vision and basic image processing. Hands-on experience with various Machine Learning libraries and frameworks, such as Numpy, Py Torch, Hugging Face, Open CV, scikit-learn, spa Cy, NLTK, etc. Experience with vector databases, LLMs, and frameworks like Lang Chain. Experience with building and maintaining APIs using frameworks such as Flask, Fast APi, Django etc Outstanding communication and collaboration skills, including the ability to convey complex technical concepts to both highly technical and non-technical audiences. Capability to work autonomously, managing multiple projects concurrently with strong organizational skills. Enthusiasm for learning and an ongoing eagerness to explore new developments in the continuously evolving field of Machine Learning and Deep Learning Experience contributing to or maintaining open-source ML projects.