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Job Description: Senior AI Engineer – Responsible AI

Role Overview

As a Senior AI Engineer specializing inResponsible AI (RAI), you will lead the integration of responsible AI principles from project inception to deployment. With a solid understanding of cloud platforms such as Azure or AWS, you will champion ethical AI practices, help enterprises mitigate AI-related risks, and drive the adoption of Responsible AI solutions. Your role will involve building automated Responsible AI guardrails, streamlining AI concepts, Detecting and Mitigating AI system risks, ensuring compliance, and strengthening service offerings. This position also requires hands-on expertise in monitoring and observability, MLOps, and AI/ML lifecycle management.

Responsibilities

Embed Responsible AI principles throughout the AI/ML lifecycle, ensuring fairness, transparency, accountability, and compliance with data privacy standards (e.g., GDPR, CCPA, HIPAA). Design, codify, and implement automated Responsible AI guardrails to monitor fairness, explainability, robustness, and safety in real time. Build and enforce Responsible AI metrics (e.g., bias, robustness, interpretability) using frameworks like Fairlearn, AI Fairness 360, SHAP, and LangChain integrations. Define, document, and propagate best practices for Responsible AI adoption, integrating technical concepts like model interpretability, adversarial robustness, and secure data handling protocols. Establish monitoring pipelines to track model performance, bias, drift, and other metrics in real time using tools like Arize AI, MLflow, and Langfuse. Develop observability solutions to improve AI/ML lifecycle management, ensuring models are explainable, reliable, and secure. Deploy Responsible AI tools, frameworks, and solutions to cloud platforms such as Azure and AWS, leveraging orchestration tools (e.g., Kubernetes, Docker) to ensure scalability, reliability, and compliance across cloud environments. Build dashboards and analytics tools to provide actionable insights into AI system behavior and performance. Implement Responsible AI measures in Agentic architectures, ensuring agent behaviors align with ethical guidelines and enterprise objectives. Develop mechanisms for monitoring and governing multi-agent systems and workflows that include autonomous LLM agents. Incorporate LLM-based evaluation mechanisms, leveraging LLMs as judges to assess model outputs for ethical and quality adherence. Lead teams to monitor emerging AI trends and advancements, ensuring the organization remains at the forefront of Responsible AI practices. Drive innovation by implementing scalable and ethical AI solutions that align with Responsible AI guidelines. Mentor junior engineers, fostering a culture of ethical AI development and knowledge sharing. Develop frameworks for reproducible, transparent, and compliant AI solutions. Collaborate with cross-functional teams to design and implement AI Ops and MLOps pipelines that integrate Responsible AI principles. Ensure pipelines automate compliance checks and streamline Responsible AI processes across the ML lifecycle. Conduct regular technical audits of ML/DL and LLM-based systems to detect and mitigate issues such as bias, drift, hallucinations, and unintended consequences. Ensure robust monitoring systems are in place to address AI risks and maintain compliance with regulatory requirements.

 

Requirements

At least 5+ years of experience in technical roles focused on AI/ML development, with a minimum of 1+ year of experience implementing Responsible AI principles. Expertise in embedding Responsible AI principles across the AI/ML lifecycle, including bias detection, explainability, drift analysis, and adherence to regulatory standards such as GDPR, CCPA, and HIPAA. Proficiency in frameworks and tools for Responsible AI, such as Fairlearn, AI Fairness 360, SHAP, and LangChain, to assess and enforce ethical AI metrics. Strong knowledge of AI risk mitigation strategies, including automated guardrails for fairness, robustness, and safety compliance. Advanced hands-on experience with Python, FastAPI, and Flask for developing scalable AI/ML solutions. Proven experience with cloud platforms like Azure and AWS, including deployment, containerization, and orchestration using Kubernetes and Docker. Extensive experience in AI/ML model lifecycle management, with practical knowledge of monitoring tools like MLflow, Arize AI, and Langfuse for observability. Expertise in building and managing MLOps pipelines, ensuring integration of Responsible AI principles and automating compliance checks. Strong problem-solving and analytical skills, with a focus on design thinking, architecture creation, and developing frameworks for reproducible and scalable AI solutions. Experience in multi-agent systems and workflows, with an understanding of agentic architectures and their alignment with Responsible AI objectives. Knowledge of emerging AI governance trends, including leveraging LLMs as judges to validate outputs and ensure ethical compliance. Excellent communication and collaboration skills, with a demonstrated ability to work in cross-functional teams and mentor junior engineers.

 

Good to Have Skills

Proficiency in design thinking and creating scalable AI architectures. Familiarity with DevOps practices, including CI/CD pipelines for AI/ML systems. Hands-on experience with advanced AI/ML model fine-tuning and optimization techniques. Exposure to frameworks like LangChain, Llamaindex.

 

 

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