AI Quality Automation Engineer – Assistant Vice President

In this AVP-level role, you will be responsible for designing, implementing, and maintaining automated testing frameworks for AI systems. You will validate model accuracy, data pipelines, and software integrations to ensure high performance, reliability, and ethical compliance across the development lifecycle. You will act as a crucial bridge between data science, software engineering, and quality assurance, working closely with data scientists, ML systems engineers, front-end engineers, and business leaders. If you are passionate about operationalizing complex models, creating highly reliable automated validation services, and defining the quality lifecycle for data science workflows at an enterprise scale, this role is a perfect fit. Key Responsibilities & Competencies Quality Framework Architecture: Design, build, and maintain scalable automated testing frameworks specifically tailored for AI/ML systems, including LLMs, RAG pipelines, and traditional ML models. Model & Output Validation: Design and execute automated evaluation pipelines to measure critical metrics such as hallucination rates, context retention, retrieval precision, semantic similarity, and answer relevance (using tools like RAGAs, DsPy). Data Pipeline & ETL Auditing: Validate the quality, integrity, and schema compliance of data pipelines and ETL processes (PySpark, Hive, Kafka, Parquet, Iceberg) to ensure clean data flows into AI models. CI/CD Integration: Integrate automated AI quality gates into enterprise CI/CD pipelines (using Tekton, Harness, UDeploy, Jenkins) to enable continuous testing and prevent regressions. Risk Stewardship & Compliance: Lead adversarial testing (Red Teaming) to identify vulnerabilities like prompt injection, jailbreaking, and data leakage. Validate real-time safety guardrails to ensure compliance with ethical AI guidelines, corporate policies, and regulatory standards. Operational Excellence & Observability: Implement monitoring and observability solutions (using MLFlow, Dagster, Vertex AI, SageMaker) to detect model drift, performance degradation, and latency bottlenecks in production. Stakeholder Engagement: Demonstrate exceptional communication and diplomacy skills, effectively engaging with cross-functional stakeholders to articulate quality risks, negotiate solutions, and align on quality benchmarks. Required Skills & Technical Qualifications Experience: 5-8 years of professional experience implementing data-intensive solutions or test automation frameworks using agile methodologies, with at least 2-3 years of direct experience validating AI/ML, Generative AI, or data-intensive systems. Programming Languages: Primary: Python, Shell Scripting, Jupyter Notebook Scripting Secondary: Java Machine Learning & AI Evaluation Frameworks: Core ML: Scikit-learn, Keras, TensorFlow, PyTorch, spaCy, NLTK Advanced NLP & GenAI Evaluation: RAGAs, DsPy, Hugging Face Transformers, RASA, Large Language Models (LLM) MLOps & ML Lifecycle Management: Platforms: MLFlow, Dagster, Google Cloud Vertex AI, Amazon SageMaker Database Management Systems: Relational: Oracle, MySQL, PostgreSQL NoSQL: MongoDB Infrastructure & DevOps: CI/CD & DevOps Tooling: UDeploy, Harness, Tekton, Jenkins, Maven Version Control: Git, Subversion (SVN) Containerization & Orchestration: Docker, OpenShift, Amazon ECS Data Engineering & Processing: Messaging & Asynchronous Communication: Kafka, RabbitMQ ETL & Data Warehousing: Sqoop, PySpark, Hive, Hadoop, HDFS File Formats: Avro, Parquet, Iceberg Job Scheduling & Orchestration: Autosys, Cron Web Technologies: Web Frameworks: FastAPI, Flask Web Servers & Reverse Proxies: NGINX Education: Bachelor’s/University degree or equivalent experience ------------------------------------------------------ Job Family Group: Technology ------------------------------------------------------ Job Family: Technology Quality ------------------------------------------------------ Time Type: Full time