AI Architect

Role Description

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
• Regulatory filings

The work is hard in ways that matter:

• Documents are messy and adversarial.
• The stakes are real (privilege, defensibility, attorney work product).
• 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
• 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
  • 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)
  • 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.

Qualifications

• 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

• 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.