Role Overview :

We are seeking an experienced Lead Responsible AI (RAI) Engineer to drive the design, implementation, and operationalization of Responsible AI, AI Safety, Quality Engineering, Evaluation, and Governance practices across Generative AI and Agentic AI solutions.

This is a hands-on technical leadership role focused on ensuring AI systems are safe, reliable, scalable, auditable, and production-ready. The role bridges AI Engineering, Quality Assurance, Governance, and Platform Operations to embed trust, compliance, and operational excellence throughout the AI lifecycle.

The ideal candidate will possess deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI systems, AI evaluation frameworks, observability, governance controls, and enterprise-grade AI deployment practices.

Key Responsibilities :

Responsible AI & Quality Engineering :

- Lead the implementation of Responsible AI, AI Safety, and Quality Engineering frameworks for GenAI and Agentic AI applications.

- Define and operationalize AI validation strategies covering :

1. Functional correctness

2. Factual accuracy

3. Hallucination detection

4. Retrieval quality

5. Prompt safety

6. Agent behavior validation

7. Failure and exception handling

- Develop testing methodologies for :

1. LLM-powered applications

2. RAG architectures

3. Agentic workflows

4. Multi-step reasoning systems

5. Tool orchestration frameworks

AI Evaluation & Governance :

- Design evaluation frameworks measuring :

1. Relevance

2. Groundedness

3. Safety

4. Consistency

5. Latency

6. Token consumption

7. Business outcome alignment

- Establish governance controls, auditability practices, traceability standards, and release-readiness criteria.

- Drive adoption of AI risk management, compliance validation, and governance-by-design principles.

Safety, Guardrails & Monitoring :

- Collaborate with engineering teams to implement :

1. Guardrails

2. Prompt controls

3. Safety mechanisms

4. Escalation workflows

5. Fallback strategies

- Enable AI observability through logging, tracing, monitoring, diagnostics, and cost tracking.

- Support incident analysis and continuous improvement initiatives.

Technical Leadership :

- Review AI solution architectures for safety, compliance, reliability, and operational resilience.

- Guide teams on testing agentic systems involving :

1. Tool calling

2. Context management

3. State persistence

4. Multi-agent coordination

5. Autonomous decision boundaries

- Mentor engineering and quality teams on Responsible AI best practices, testing methodologies, and production monitoring approaches.

- Develop reusable accelerators including :

1. Test harnesses

2. Evaluation templates

3. Governance checklists

4. Safety review frameworks

5. Red-team testing assets