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About KLDiscovery
KLDiscovery is a global eDiscovery and legal technology provider serving large law firms, corporate legal departments, and government agencies. We build and operate the products and services that legal teams rely on to manage, process, and review case data at scale. With operations across multiple countries and a client base that includes AmLaw 200 firms, we handle some of the largest and most complex matters in the industry.
About the Role
We're hiring our most senior AI practitioner. As AI Architect, you'll own the north star for gen AI and ML engineering at KLDiscovery and build alongside the team to make it real.
This is one of the most interesting AI problem sets in enterprise software. You'll work with terabytes of real-world legal data, including emails, contracts, chat transcripts, images, video, depositions, and regulatory filings from some of the largest litigation and investigation matters in the world. The work is hard in ways that matter: documents are messy and adversarial, the stakes are real (privilege, defensibility, attorney work product), and the upside is enormous. AI that can surface key people, themes, and timelines in hours instead of weeks, or pre-classify millions of documents for relevance and privilege, directly changes the economics of how legal matters get resolved.
This is a builder-first role. You'll design and ship agent workflows that take on attorney-level work, retrieval systems that reason over case data, and evaluation harnesses that prove our AI is defensible in court. You'll set strategic direction across Nebula, our eDiscovery platform, and CS & Operations, then prove the architecture by building the hardest parts yourself. Not a role for anyone stepping back from the keyboard.
We offer competitive total compensation that includes base pay, bonus potential, inclusive benefits, wellness programs, and perks. We use market and industry data to inform pay decisions while considering geography and labor markets, individual experience, and business needs. Individual compensation will vary, although a reasonable estimate of the current annualized base pay range for this position is $190,000 to $230,000.
Job location: Remote (but candidate must be based in the United States)
Key Responsibilities:
Own the AI-native architecture and build it. Define the end-to-end gen AI architecture across Nebula and CS & Operations, covering LLMs, agent harnesses, RAG, vector search, embeddings, and model selection and triage. Build the hardest parts personally: prototype agent loops, tune retrieval, design evals, and ship the shared infrastructure that powers AI Case Explorer (case overviews, timelines, key people and themes, PII surfacing, and Agent chat), AI Agent Review (pre-classifying relevance, privilege, and key issues, shipping MLP), and CS & OPS tech-enablement using AI as a core part of our central work orchestration system.
Own AI/MLOps and AI telemetry end-to-end. Model deployment and versioning, eval pipelines, drift and quality monitoring, cost and latency telemetry, and prompt and agent observability. Define and implement how we select, triage, and route across models (Azure OpenAI, Anthropic, open-source, fine-tuned), manage vector databases and retrieval, and evolve our agent harness as the frontier moves.
Lead the AI practice from the front. Set the technical bar by building, not by reviewing. Partner with Engineering, Product, and Data Science leadership to translate architecture into delivery. Raise the bar on AI engineering, mentor senior ICs through hands-on technical leadership, and represent KLD's AI strategy with customers, partners, and at industry events.
What You Bring (required skills):
7+ years in machine learning, applied AI, or ML engineering, with recent hands-on experience as a senior or principal-level builder in the gen AI era
Proven track record architecting and personally building enterprise gen AI systems in production with customer impact
Builder at heart: still writes code, ships, and tunes prompts and evals, and wants to keep doing so as a leader
Deep expertise across the modern gen AI stack: LLMs, agents, RAG, vector databases, embeddings, search, and evaluation harnesses
Hands-on experience designing system-of-systems AI pipelines spanning search, retrieval, agent harnesses, and model selection/triage
Strong proficiency with the Microsoft AI stack: Azure OpenAI, Azure AI Foundry, Azure AI Search, and supporting Azure infrastructure
Experience owning MLOps and AI telemetry: model deployment, eval pipelines, monitoring, drift detection, and prompt/agent observability
Excellent technical leadership skills; demonstrated ability to influence architecture decisions across product and engineering
Strong communication skills, including explaining AI architecture trade-offs to executive and customer audiences
Nice to Have (preferred skills):
Advanced degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or related field
Background building agentic systems with tool use, planning, and multi-step reasoning in production
Prior experience setting up AI governance and evaluation harnesses
Open-source contributions, technical writing, conference talks, or other evidence of being a recognized builder in the AI community
