Job Description
Our client is building the next generation of legal services through the responsible, strategic use of artificial intelligence. We are seeking AI Engineers who are proven software builder first-someone who can write production-grade code and has evolved into modern AI development. These are engineers that will build real applications on top of existing LLMs. The role can sit fully remote, but preferred to be by an office location: Boston MA, NYC, Palo Alto CA, Chicago IL, Denver CO, LA, Miami FL, Reston VA, San Diego CA, San Francisco CA, Santa Monica CA, Seattle WA, or Washington DC.
The firm is deeply rooted in innovation, they leverage AI to enhance legal quality, accelerate client outcomes, and deliver sophisticated solutions to the world's most forward thinking technology companies. AI is embedded across client services and internal platforms, guided by clear principles around ethics, transparency, data security, and human centered design. AI at the firm is not about replacing judgment, it is about amplifying expertise, enabling smarter decision making, and setting the standard for what a modern, technology enabled law firm can be. They are widely recognized as a top tier AI law firm advising companies developing and deploying AI. They publicly describe themselves as "counsel behind the code" and emphasize deep fluency in LLMs, machine learning, model governance, data privacy, and commercialization - ranked Band 1 nationwide for Artificial Intelligence by third-party and has represented clients in precedent setting AI litigation, including copyright, model training, and data scraping disputes.
They have invested directly in AI powered tools and platforms to enhance client delivery, including: a platform that offers AI enabled legal tools, document generators, and curated guidance for startups and high growth companies, and an AI powered chatbot designed to surface legal insights and firm content more efficiently. These offerings are positioned as ways to speed decision making and push legal work forward, not just marketing showcases.
This role is hands-on AI engineering. This team functions like a startup but backed by a 2.5 Billion dollar powerhouse. We are looking for strong folks who can help build foundations for guardrails and enterprise-ready patterns (security, reliability, maintainability) aligned with the firm's responsible AI posture. We are seeking a highly experienced AI Engineer to design and build real-world applications powered by large language models (LLMs). This role focuses on developing scalable, production-grade solutions that leverage modern AI platforms while maintaining strong software engineering principles.
This is not a research-based role-candidates will focus on applying AI to solve complex business problems through well-architected systems.
One use case is around billing inefficiency, where the firm is losing roughly ~6% of revenue due to issues in how time is recorded, structured, and billed. - they are looking to build an AI-enabled system that can: analyze large volumes of billing and contract data; identify exactly where and why revenue is being lost; categorize inefficiencies (e.g., poor descriptions, timing issues, discounts) and allow users to interactively query the data through a chat interface. 2Key Responsibilities
-Design and build AI-enabled applications and workflows
-Develop integrations with leading LLM platforms (e.g., OpenAI, Anthropic, AWS AI services)
-Translate business problems into technical solutions
-Architect systems that:
Combine structured and unstructured data
Balance AI and traditional logic
Build scalable backend services using Python and related technologies
Implement DevOps best practices for code deployment and release pipelines
Collaborate with engineering and business stakeholders to deliver high-impact solutions
Optimize AI usage for: Accuracy, Cost efficiency, and Reliability
Compensation:
$75/hr to $85/hr.
Exact compensation may vary based on several factors, including location, skills, experience, and education.
Employees in this role will enjoy a comprehensive benefits package starting on day one of employment, including options for medical, dental, and vision insurance. Eligibility to enroll in the 401(k) retirement plan begins after 90 days of employment. Additionally, employees in this role will have access to paid sick leave and other paid time off benefits as required under the applicable law of the worksite location.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
10+ years building software across modern stacks and clear evidence of transitioning into AI development (not just prompt usage).
7+ years in Python engineering
BS (or higher) in Computer Science.
Proven ability to produce and write code at the console level (Python, Java, C++, Scala, Go, etc)
2+ years AI hands on experience with frontier models (OpenAI, Anthropic, Gemini, Llama, DeepSeek, Meta, Mistral, Mixtral, Grok, Vertex AI, etc)
Strong understanding of machine learning algorithms, deep learning, and data mining techniques
MLOps and Deployment: Experience with model lifecycle management tools (e.g., MLflow), Docker, and cloud computing for production deployment
LLM and Generative AI: Knowledge of implementing LLMs using vector bases, Retrieval-Augmented Generation (RAG), and tuning models like GPT or LLaMA
Cloud experience (AWS preferred, Azure, GCP, etc) - AWS AgentCore
Familiarity with modern "AI-assisted analytics" ecosystems (e.g., lakehouse / governed semantic layers) in professional services environments.
Knowledge graphs and graph query patterns (e.g., AWS Neptune, Gremlin/Cypher concepts
