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.