AI Lead

Responsibilities

Job Description:

Lead the end-to-end design and architecture of autonomous and semi-autonomous AI agents (including multi-agent systems) that orchestrate complex, cross-functional business workflows.

Own strategic and hands-on technical decisions for models, orchestration frameworks, vector databases, runtime platforms, observability tooling, and policy/guardrail layers to balance performance, safety, and cost.

Drive the full lifecycle of Agentic AI solutions, from PoCs through pilots, production rollout, and continuous optimization.

Build, mentor, and retain a high-performing team of ML/AI engineers, agent/prompt engineers, and product partners focused on delivering high-value agentic use cases.

Define, implement, and enforce governance for safety, security, privacy, and ethics of autonomous agents, including human-in-the-loop controls, escalation paths, and clear accountability for decisions.

Partner with security, risk, legal, and compliance functions to embed organizational policies and external regulations into agent behavior, access controls, and data handling.

Design and implement robust monitoring and observability for agents, covering reliability, accuracy, latency, drift, hallucination patterns, business KPIs, and critical failure modes, with clear remediation runbooks.

Required Qualifications

7+ years in software, data, or ML/AI engineering, including several years owning AI or automation initiatives end-to-end.

Strong hands-on experience with modern AI: LLMs, retrieval‑augmented generation, tool use, or autonomous agent frameworks.

Proven ability to design and deliver production AI solutions integrated into real-world business processes and systems.

Experience leading technical teams and collaborating closely with product, operations, and executive stakeholders.

Solid understanding of AI risk, safety, security, and data privacy concepts, and how to implement guardrails in practice.

Familiarity with cloud platforms (AWS/Azure/GCP), MLOps practices, and event-driven or microservices architectures.

Preferred Qualifications

Prior work in a Center of Excellence or incubator role, establishing standards and reusable AI assets.