Responsible AI Engineer

Eligibility Criteria & Experience

Experience in software engineering or ML engineering, with hands-on exposure to AI safety, governance, or trust engineering.

Proven experience building and deploying LLM or agentic AI systems in enterprise or regulated environments.

Strong expertise in guardrails engineering, red teaming, bias/fairness evaluation, or AI regulatory compliance.

Working knowledge of AI regulatory frameworks such as EU AI Act, NIST AI RMF, ISO/IEC 42001.

Strong Python programming skills with experience building evaluation pipelines, observability systems, or governance tooling.

Role Overview & Job Description

As a Responsible AI Engineer, you will make an impact by engineering the trust, safety, and governance layer for agentic AI systems deployed at enterprise scale.

You will work collaboratively with AI engineers, architects, risk teams, and client stakeholders to ensure every AI solution is safe, compliant, and production-ready.

Key Responsibilities

Design and implement runtime guardrails and safety architectures for LLM and agentic AI systems.

Lead red-teaming and adversarial testing initiatives to identify vulnerabilities.

Build and operationalize AI governance and compliance frameworks.

Develop and deploy fairness, bias, and model evaluation pipelines.

Establish observability and auditability for AI systems through structured logging, audit trails, and governance metrics.

Required Technical & Soft Skills

Technical: AI Safety, LLM systems, Guardrails engineering, Red teaming, Python, Evaluation pipelines, AI governance tooling.

Soft Skills: Collaboration with security & risk teams, Executive-level presentation skills.

Important Notes & Details

Work Mode: Hybrid (Onsite position requiring 5 days a week)

Shift Details: Regular business hours

Applicants may be required to attend interviews in person or by video conference.