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About Syllo

Syllo is on a mission to transform litigation. Our product is an AI-powered litigation workspace that enables lawyers and paralegals to safely harness the power of language models throughout the litigation life cycle. Since going to market, we have gained a diverse group of enterprise customers, including some of the biggest law firms in the country, and we are quickly expanding. By reducing the expense of litigation industry-wide, we aim to improve access to high-quality representation and promote the alignment of legal outcomes with merit.

We are a mission-driven venture and recognize the enormous societal benefits that will result from a legal system that works better for everyone.

We are seeking a skilled and fast-moving Senior AI Engineer to join our team. This role is focused on the practical application of AI, specifically leveraging Large Language Models (LLMs), to build robust, scalable, and impactful user-facing features. This is an engineering-centric role—you will be an expert at building and integrating production systems, not primarily a research scientist.

The Responsibilities

• System Design & Implementation: Design and build high-performance, production-level AI-powered features and services, with a focus on reliability and scalability.

• Develop robust engineering solutions to mitigate the inherent unreliability of LLMs. This includes implementing effective guardrails, validation pipelines, error handling, caching, and failover/fallback mechanisms to ensure a high-quality user experience even when the model misbehaves.

• Focus on optimizing latency, throughput, and cost efficiency for all AI-powered services.

• Serve as an engineering expert in integrating models and data feeds with existing business logic and APIs.

• Engineering Best Practices & Quality:

• Champion and enforce high standards for code quality, architectural design, and system documentation.

• Implement rigorous testing strategies, including unit, integration, and performance testing, to ensure the robustness of AI features before and after deployment.

• Participate actively in peer code reviews to maintain a high bar for engineering standards across the team.

• Feature Delivery & Ownership:

• Take ownership of the full lifecycle of new AI features, from initial design and prototyping to testing and deployment into the production environment.

• Own the end-to-end development of new features, including API design, integration with front-end services, persistent data storage, and comprehensive monitoring/alerting.

• Work closely with product managers and other engineers to scope, estimate, and deliver features on a fast iterative cycle.

• Pipeline Enhancement: Quickly onboard onto our existing codebase to understand, maintain, and significantly enhance existing data and feature pipelines, improving their efficiency and robustness.

• Tooling & Velocity: Embrace and utilize AI-powered developer tools (such as GitHub Copilot) to maximize your efficiency, demonstrate a commitment to continuous learning, and set a high bar for engineering velocity.

Qualifications

• Deep Engineering Fluency & Quality Focus: Proven ability to build and deploy complex, high-quality software systems with an emphasis on maintainable code, rigorous testing, and engineering best practices.

• Python Proficiency: Fluent in Python and its ecosystem for backend services and data processing.

• System Design Expertise: Strong grasp of system design principles, including distributed systems and API design.

• Demonstrated ability to design scalable distributed systems, including expertise in microservices architecture.

• Codebase Agility: Excellent ability to read, understand, and navigate an unfamiliar codebase quickly.

• Fast Learner: A demonstrable history of rapidly acquiring new technical skills and applying them to solve business problems.

Great to Have

• AI/ML Feature Experience: Experience directly building, shipping, and maintaining AI-powered products, agents, or intelligent features in a commercial setting.

• LLM/Generative AI Exposure: Familiarity with the unique engineering challenges of integrating Large Language Models into a production environment.

• Hands-on experience with LLM operationalization challenges (MLOps for LLMs), including prompt engineering, working with vector databases, RAG systems, or managing context windows.

• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) for deploying and managing services.