AI Engineer (Applied LLM Systems)
About Discernis
Discernis builds AI driven document intelligence for high stakes legal work. Because our customers handle privileged and regulated matters that most cloud AI cannot touch, we run on premises and in customer controlled environments as well as in the cloud. That makes security, reliability, and reproducibility core product features, not afterthoughts. We work with AmLaw firms and enterprise legal teams where accuracy, explainability, and data control are non negotiable.
The Role
You will lead the design and evolution of the AI systems at the core of our product. Day to day, that means inventing new AI powered workflows that help legal teams understand massive document sets, then making them faster and more reliable, and building the evaluations that tell us we are getting it right. You will reach for whatever technique fits the problem, prompt engineering, agentic orchestration, retrieval, or post-training, and because our customers often cannot send data to external providers, much of this runs on models we host and improve ourselves.
What You Will Do
Design and build new AI powered workflows that solve real problems for legal teams, using whatever gets the best result: agentic and multi step orchestration, prompt engineering, retrieval, and post-training
Improve the quality, reliability, latency, and cost of existing workflows using that same full toolkit
Own and develop our evaluation and benchmarking frameworks so we can measure and improve quality, catch regressions, and know we are moving in the right direction
Design agentic patterns and structured outputs robust enough for complex legal and investigative use cases
Own self hosted model serving and inference optimization across customer deployments
Post-train and adapt models where that is the right lever, including fine tuning, preference optimization, and domain adaptation
What You Bring
Experience building production LLM or ML powered features end to end, with the judgment to know which technique fits which problem
Hands on experience with agentic or multi step LLM orchestration and tool use
Breadth across the modern LLM toolkit: prompt engineering and retrieval, and ideally post-training methods such as fine tuning, DPO, or RLHF
Experience designing and running evaluations for LLM systems
Hands on experience with self hosted model serving such as vLLM, TGI, or similar
Strong Python skills and comfort with concurrent or distributed processing
Bonus: embedding and vector search pipelines, or retrieval augmented generation at scale
Bonus: background in legal tech, document intelligence, or information retrieval
Tech Environment
Python, self hosted inference (vLLM), LLM orchestration and agent frameworks, evaluation tooling.
Skills Required
- Production experience building LLM- or ML-powered features end to end
- Hands-on experience with agentic or multi-step LLM orchestration and tool use
- Experience with prompt engineering and retrieval
- Experience designing and running evaluations for LLM systems
- Hands-on experience with self-hosted model serving such as vLLM, TGI, or similar
- Strong Python skills and comfort with concurrent or distributed processing
- Experience with post-training methods such as fine-tuning, DPO, or RLHF
- Experience with embedding and vector search pipelines or retrieval-augmented generation at scale
- Background in legal technology, document intelligence, or information retrieval
What We Do
Discernis builds AI-native document-intelligence software for legal professionals handling litigation, investigations, and other high-stakes work. Its platform analyzes entire document collections—often millions of files—using cross-document analysis to surface key evidence and inform case strategy. Designed for security-sensitive customers, it can run on premises, in a customer-controlled cloud, or in hybrid environments, keeping inference within the organization rather than using third-party AI APIs.
