AI Quality Automation Engineer – Assistant Vice President

Citibank (Switzerland) AG; Pune District; On-site; Confidential; Full time

Job summary

Citi seeks an AVP-level professional to design, implement and maintain automated testing frameworks for AI systems in Pune. You will validate model accuracy, data pipelines, and software integrations to ensure performance, reliability, and ethical compliance across the development lifecycle.

You will bridge data science, software and QA, collaborating with engineers, data scientists and line leaders to operationalize complex models and deliver robust validation services at enterprise scale.

Qualifications

• 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: Python, Shell Scripting, Jupyter Notebook Scripting; Java as secondary.
• Machine Learning & AI Evaluation Frameworks: Scikit-learn, Keras, TensorFlow, PyTorch, spaCy, NLTK; Advanced NLP & GenAI Evaluation: RAGAs, DsPy, Hugging Face Transformers, RASA, LLMs.

Responsibilities

• Quality Framework Architecture: Design, build, and maintain scalable automated testing frameworks for AI/ML systems.
• Model & Output Validation: Create automated evaluation pipelines for metrics like hallucination rates and semantic similarity.
• Data Pipeline & ETL Auditing: Validate data pipelines and ETL processes (PySpark, Hive, Kafka, Parquet, Iceberg).
• CI/CD Integration: Integrate automated AI quality gates into enterprise CI/CD pipelines (Tekton, Harness, UDeploy, Jenkins).
• Risk Stewardship & Compliance: Lead adversarial testing to identify vulnerabilities and ensure ethical AI compliance.
• Operational Excellence & Observability: Implement ML-driven monitoring to detect drift and latency in production.

Skills

Python

Shell scripting

Jupyter scripting

Java

Scikit-learn

TensorFlow

PyTorch

RAGAs

DsPy

NLP

Education

Bachelor’s degree

Tools

Tekton

Harness

UDeploy

Jenkins

Docker

OpenShift

Kafka

Hive

Job description

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

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Job Family Group: Technology

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Job Family: Technology Quality

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Time Type: Full time

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Most Relevant Skills Please see the requirements listed above.

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Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.

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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster.