Senior AI Architect - GenAI/LLM

ESSENTIAL DUTIES AND RESPONSIBILITIES:

1. AI Architecture - Technical Strategy:

- Own architecture decisions for AI features on AIS's platform: LLM/model selection, RAG and agentic design, vector store and retrieval strategy, MCP-based integration.

- Set reusable design standards - prompt/context management, evaluation, guardrails - so features aren't built as one-offs.

- Make build-vs-buy and vendor calls (OpenAI, Anthropic, Azure OpenAI, automation platforms) on cost, latency, accuracy, and lock-in.

2. Zero-to-One Delivery - Scaling:

- Take AI ideas from prototype to a production system that holds up under real case volumes and audit scrutiny.

- Define AIS's bar for "production-ready": accuracy thresholds, latency budgets, human-in-the-loop fallback, monitoring, rollback plans.

- Own the path from single-workflow pilot to full-population, multi-client rollout, including the data pipeline and infra work scale requires.

3. Security, Compliance - Governance:

- Own security and privacy posture for every AI system touching client data, consumer PII, and case information - access controls, encryption, vendor data-handling terms, retention.

- Build and enforce an AI governance framework: model risk classification, human-oversight checkpoints, audit logging, explainability.

- Partner with compliance, legal, and security (internal and client-side) so AI features clear regulatory bar before shipping.

4. Engineering Leadership:

- Hire, mentor, and set the technical bar for a team of AI/GenAI engineers and automation developers; review architecture and code at key checkpoints.

- Turn ambiguous business asks into scoped workstreams with clear ownership and definition of done; run delivery across concurrent initiatives.

5. Stakeholder Communication:

- Be the primary technical voice on AI to the senior leaders - explain trade-offs, risk, and ROI in terms an executive audience can act on.

- Collaborate and defend roadmap materials (capability-to-lifecycle mapping, Now/Next/Later views); support client/RFP conversations where AI is a differentiator.

- Push back on scope or timeline when the technical reality doesn't match the ask.

6. MLOps - Production Operations:

- Own monitoring, drift tracking, incident response, and continuous evaluation for every deployed AI feature.

- Track the metrics that matter per feature (accuracy, cost per transaction, latency, escalation rate) and drive iteration on them.

Required Skills:

- 10+ years in software/AI engineering, including 4+ years hands-on GenAI/LLM or applied ML, with proven ownership of architecture - not just implementation - for production AI systems.

- Track record taking at least one AI/automation product from concept to production at meaningful scale.

- Hands-on with LLM integration (OpenAI, Anthropic/Claude, Azure OpenAI), RAG and vector databases, and agentic/orchestration frameworks (LangChain, LangGraph or similar), including MCP-based integration.

- Experience owning security, data governance, or compliance posture for AI systems in a regulated or sensitive-data environment (financial services, healthcare, insurance strongly preferred).

- Demonstrated people leadership - has hired, mentored, and directly managed engineers, not just coordinated a project team.

- Direct experience presenting to senior/executive stakeholders - roadmaps, architecture decisions, or vendor evaluations at that level.

- Strong fundamentals: Python and/or C#/.NET, REST APIs, cloud (Azure/AWS/GCP), Docker/Kubernetes, CI/CD.

Qualification:

- Bachelor's or Master's in Computer Science, AI/ML, Data Engineering, or related field.

Preferred Skills:

- Prior experience in fintech, lending, banking operations - servicing, insurance, or another regulated financial-services domain.

- Experience building or governing an AI Center of Excellence, model risk framework, or Responsible AI program.

- Experience automating compliance-adjacent workflows (call/quality monitoring, document review, claims processing) via integration with enterprise systems.

- Relevant certification (Google Cloud, Microsoft Azure AI, or equivalent).