Senior ML Engineer

About the job

Role Overview You will lead the creation and productionization of our AI-driven search pipeline — from building vector indexes and deploying RAG-based systems to designing scalable APIs. You’ll work closely with our engineering team to ingest structured legal data, vectorize it, and ensure seamless integration with our user-facing web application. This role requires both deep technical expertise, a product-focused mindset and an enthusiasm to learn new techniques in the fast changing AI landscape. Key Responsibilities 1. AI Based Search Development & Optimization • Design and build AI-powered search models that improve retrieval and ranking of legal documents. • Implement retrieval-augmented generation (RAG) workflows using pre-trained LLMs (e.g., OpenAI GPT-4). • Fine-tune LLMs for legal use cases where necessary (experience with custom LLM training is a strong plus). • Improve search quality through relevance testing, feedback loops, and query understanding. • Research and implement any new techniques for improving search result relevancy. 2. Data Processing & Vector Indexing • Build pipelines to ingest, chunk, and vectorize legal texts (case law, statutes, etc.). • Create and maintain indexes in Vector Databases, supporting fast and relevant results. • Maintain an evolving legal search index by ingesting new documents on a weekly basis. 3. Model Deployment & API Development • Deploy ML models into production using Azure cloud infrastructure. • Develop REST APIs (with FastAPI or Flask) to expose model functionality to the application layer. • Monitor and optimize latency, scalability, and reliability of deployed solutions. 4. Collaboration & Product Integration • Work closely with product managers and full-stack engineers to ship ML-backed features. • Participate in design reviews and own technical decisions around AI architecture. • Track and improve system performance using user feedback, telemetry, and experimentation. Tech Stack & Tools • ML/NLP: Python, PyTorch/TensorFlow, Hugging Face, Azure OpenAI APIs • Vector Search: Azure AI Search (primary), experience with FAISS, Pinecone or Elasticsearch a plus • Deployment: Azure (App Services, Azure Functions, Blob Storage, Key Vault) • Data Processing: Pandas, NumPy, spaCy, NLTK • APIs: REST APIs built with FastAPI or Flask Required Skills & Experience • 5+ years of experience in machine learning, NLP, or AI-based search systems. • Strong knowledge of vector search, document embeddings, and retrieval techniques. • Experience building and scaling RAG pipelines with LLMs. • Proficiency with Azure AI Search for document indexing and search optimization. • Demonstrated ability to deploy models to production and build robust APIs. • Familiarity with search ranking algorithms (BM25, hybrid search, learning-to-rank). • Experience working with document-heavy datasets in legal, academic, or enterprise domains. • Experience with fine tuning models and creation of datasets used in fine tuning. Nice to Have • Background in legal tech, contract analysis, or legal document retrieval. • Exposure to open-source search frameworks like Elasticsearch or OpenSearch. • Knowledge of observability, logging, and system performance profiling.

Requirements

• Machine Learning
• NLP
• AI-based Search
• Python
• Azure

Preferred Technologies

• Machine Learning
• NLP
• AI-based Search
• Python
• Azure