AI Architect

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
• 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