Senior AI Engineer

About The Job:

• We are hiring a Senior AI Engineer to design and ship the AI systems our lawyers and business teams use every day: agentic systems that carry out multi-step work under supervision, retrieval-augmented generation over dense legal corpora, and the evaluation discipline that tells us any of it is actually working.
• You will own solutions end to end, from architecture and pipeline design through to a production service that is monitored, evaluated and improved.

Essential Job Functions:

• Architect and build production agentic and RAG systems over the firm's documents and know-how: harness and control loops, tool design, retrieval, orchestration, and the evaluation harness that proves it works.
• Design the pipelines end to end, from the data sources in SharePoint and the wider Microsoft 365 estate through to the service the user touches, and make the architecture decisions that keep it fast, secure and affordable at firm scale.
• Own what you build end to end: architecture, code, deployment, monitoring and the improvements that follow. You write the code and you stay accountable for it once it is live.
• Prototype fast. Get something working in front of real users in weeks, not quarters, then decide what to harden, what to scale and what to throw away.
• Sit with partners, associates and business services teams, listen past the stated request to the real problem, and come back with a shaped solution and honest trade-offs.
• Run structured comparisons of models, frameworks, retrieval strategies and orchestration patterns, and turn them into a recommendation the firm can stand behind.
• Set the technical bar for AI work across the team: design reviews, code reviews, evaluation standards and the patterns other engineers reuse.
• Mentor engineers and data scientists, review their work generously, and hand them problems that stretch them.
• Share what you learn: internal write-ups, brown-bag sessions and presentations to audiences from a two-person product team to a global practice group.

Qualifications:
Core Technical Experience

• Agentic AI: Designing agent harnesses and control loops, tool and function calling, planning and memory, multi-agent patterns, human-in-the-loop checkpoints and guardrails, and keeping cost, latency and failure modes under control in production.
• Microsoft Agent Framework (MAF): Hands-on experience building with MAF and its Semantic Kernel and AutoGen lineage, and fluent enough in comparable agent SDKs to argue for the right one rather than the familiar one.
• Model Context Protocol (MCP): Building and consuming MCP servers and clients, designing clean tool and resource surfaces, and using MCP to connect agents to firm systems in a standard, reusable and governable way.
• Retrieval-Augmented Generation (RAG): Chunking and ingestion strategy, embeddings, hybrid keyword and vector retrieval, reranking, grounding and citation, and knowing why a given retrieval design fails on legal documents.
• Evaluation: Building eval sets and harnesses, offline and online evaluation, LLM-as-judge and its limits, regression testing across model and prompt changes, and metrics that a business owner recognizes as meaningful.
• Prompt Engineering and Context Engineering: Designing what the model sees, structuring and compressing context, managing long-context and multi-turn state, and systematic iteration rather than trial and error.
• Token and Cost Optimization: Caching, context pruning, routing between models by task, batching, and measuring the cost and latency of a system rather than guessing at it.
• Model Landscape Expertise: Broad knowledge of GPT and other frontier models, Claude, and open-source models, with informed views on where each earns its place and how to move between them without rewriting the system.
• Fine-Tuning Small Language Models: Experience determining when a fine-tuned small model is more effective than a large general model, including dataset creation, training, evaluation, and deployment.
• Python & Production Engineering: Expert Python programming with clean, tested, typed code, containerized services, CI/CD, workflow orchestration, Azure cloud deployment, observability, tracing, and live evaluation.
• System Architecture: Expertise in designing ingestion and indexing pipelines, service boundaries, interfaces, data flow, storage architecture, caching, queuing, batch versus real-time processing, security, latency, and cost optimization.
• SharePoint & Microsoft 365: Experience building ingestion pipelines over SharePoint and OneDrive using Microsoft Graph APIs, including libraries, site columns, content types, managed metadata, and permission trimming.
• Knowledge Graphs: Ontology and schema design, entity resolution, graph construction from unstructured text, and graph-based retrieval mechanisms.
• Foundational Expertise: 12+ years across data science, machine learning, and software engineering with strong background in classical machine learning, statistics, and experimental design.

Background

• 12+ years of professional experience across data science, machine learning and software engineering, with a recent track record of AI systems successfully deployed into production.
• A scientist's background is strongly preferred: PhD or equivalent research experience in Machine Learning, Computer Science, Computational Linguistics, Statistics, Physics, or a related quantitative discipline.

Creativity and Innovation

• Follows the latest AI trends, research papers, and industry releases while developing independent perspectives.
• Applies new technologies to real-world business problems and evaluates them honestly based on outcomes.
• Identifies opportunities proactively and develops prototypes to validate innovative concepts.
• Designs modular architectures that can evolve as AI technologies continue to advance.

Communication and Collaboration

• Communicates effectively with senior stakeholders, including partners, practice leaders, and business services teams, while balancing technical depth with business clarity.
• Strong collaboration, mentoring, presentation, and stakeholder management capabilities.