Senior AI Research Engineer

The Senior AI Research Engineer at Lawhive is responsible for conducting applied research on LLM-based reasoning, developing bespoke models for automating legal workflows, and building prototypes that explore innovative AI-driven legal solutions. You will also design RAG pipelines and optimize LLM inference using advanced techniques, ensuring that research translates into practical applications.

To qualify as a Senior AI Research Engineer at Lawhive, you should have an MSc or PhD in AI, Machine Learning, Computer Science, or a related field. Additionally, expertise in AI research, applied machine learning, NLP, and experience with LLM adaptation and inference optimization is highly valued. Proficiency in Python and familiarity with relevant technologies are also critical.

Key technical skills for the Senior AI Research Engineer role at Lawhive include proficiency in Python, FastAPI, and Pydantic, as well as hands-on experience with LLM APIs like OpenAI and Mistral. A solid understanding of retrieval-augmented generation (RAG), vector databases, and embeddings, alongside experience in deploying AI models in cloud environments, is essential for success in this position.

At Lawhive, we prioritize the professional growth of our employees by providing opportunities for collaboration and innovation within a supportive environment. Senior AI Research Engineers can engage in cutting-edge projects, participate in team-building activities, and benefit from an annual retreat, all while being encouraged to stay current with the latest advancements in AI technologies.

As a Senior AI Research Engineer at Lawhive, you will work on transformative AI solutions aimed at automating legal workflows, enhancing user experience through LLM-based AI assistants, and integrating multi-modal and external knowledge sources to improve AI-driven insights. Your role will directly contribute to addressing pressing societal issues related to legal access.

When answering this question, present specific instances where you adapted LLM models for particular tasks, explaining your methodologies and the outcomes. Highlight any key challenges you faced, how you overcame them, and the impact your work had on the projects you were involved in.

Discuss your approach to optimizing LLM inference, detailing specific techniques like LoRA, PEFT, or caching. Provide examples of how you've applied these techniques in past projects and the benefits achieved, such as reduced latency or increased accuracy in task execution.

Mention your methods for staying current, which may include reading research papers, participating in online forums, attending conferences, and connecting with peers in the industry. Emphasize any specific recent advancements you have implemented in your work.

Answer by explaining the concept of RAG, how it combines retrieval and generation in AI workflows, and its relevance to legal applications. Offer examples from your experience where RAG improved outcome accuracy or efficiency in AI-driven projects.

Provide details of your experience deploying AI models on platforms like AWS or GCP. Discuss the tools and methodologies you employed, as well as any challenges and how you overcame them, emphasizing the impact of your deployment on project success.

Discuss your collaborative philosophy, emphasizing openness to ideas, effective communication, and the importance of drawing on diverse expertise. Provide an example of a successful collaborative project where teamwork led to innovative solutions.

Elaborate on your experience in creating prototypes, detailing your process from ideation to implementation. Share example projects, what technologies you used, and feedback you received during testing phases which informed your final product.

Describe a specific instance where you applied AI to tackle a complex challenge. Focus on the problem, your analytical process, the solution implemented, and the results achieved. This showcases your critical thinking and AI expertise.

Talk about the concept of autonomous agentic AI, how it operates in complex workflows, and its potential applications particularly in legal contexts. Draw on your own experiences with multi-agent systems to illustrate your points.

Share your strategy for bridging the gap between academic research and practical applications. Highlight experiences where you successfully adapted research findings into usable solutions, discussing the challenges faced and how you ensured successful implementation.