AI/ML Architect

Client

Our client is a leading legal recruiting company aiming to build a data-driven platform specifically designed for lawyers and law firms. The platform brings everything together in one place — news and analytics, real-time deal and case tracking from multiple sources, firm and lawyer profiles enriched with cross-linked insights, rankings, and more.

Project overview

The platform aggregates data from hundreds of public sources, including law firm websites, deal announcements, legal databases, and media publications, creating a unified ecosystem of structured and interconnected legal data.

It combines AI-driven enrichment, automated data processing, and scalable infrastructure to ensure comprehensive and reliable coverage of the legal market.

Position overview

We are looking for a Lead AI/ML Engineer who will also take ownership of AI architecture, technical strategy, and team mentorship.

Responsibilities

• Develop and deploy AI/ML models leveraging GenAI (OpenAI API) for natural language understanding, summarization, and insights extraction
• Build named entity recognition (NER) and entity linking solutions tailored to legal domain data (law firms, cases, deals, people)
• Implement scalable NLP pipelines for processing news, legal documents, and transaction data from multiple sources
• Design, train, and evaluate ML models to improve search, classification, and recommendation features
• Collaborate with AWS teams to deploy and maintain models using SageMaker, manage datasets on S3, and ensure reliable operation and scalability
• Integrate AI capabilities seamlessly into the web platform, working alongside front-end and backend engineers
• Continuously research new AI models and NLP techniques relevant to legal data and user experience

Requirements

• Strong experience designing and implementing end-to-end AI/ML solutions in production environments.
• Hands-on experience with GenAI, including practical usage of the OpenAI API.
• Solid background in NLP, with hands-on experience in Named Entity Recognition (NER) and entity linking.
• Hands-on experience working with AWS, including services such as SageMaker and S3.
• Strong Python skills with practical experience using TensorFlow, PyTorch, and Hugging Face Transformers.
• Practical experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Proven experience building and implementing Recommendation Engines.

Nice to have

• Experience architecting scalable AI/ML systems on Amazon Web Services.
• Experience working with complex, data-intensive machine learning pipelines.
• Experience combining NLP, GenAI, and recommendation systems within a single solution.