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Tier-1 brand, mid-level, metro role with broad ML, data, and automation requirements.
Requires specialized ML/GenAI validation and data pipeline expertise, limiting cross-industry transferability.
Explicit 5–8 years plus mandatory ML evaluation, data pipeline, and CI/CD skills.
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Design, implement, and maintain automated testing frameworks for AI/ML systems including LLMs and RAG pipelines to ensure model accuracy and reliability.
Validate data pipelines and software integrations, integrate AI quality gates into CI/CD pipelines, and monitor model performance and drift in production.
Lead adversarial testing for AI system vulnerabilities and ensure compliance with ethical standards and regulatory requirements while collaborating across data science, engineering, and business teams.
5-8 years experience in data-intensive solutions or test automation with 2-3 years directly validating AI/ML or Generative AI systems.
Proficiency in Python, Shell scripting, Java, and experience with ML frameworks like TensorFlow, PyTorch, and NLP tools such as spaCy, NLTK.
Experience with CI/CD tools (Tekton, Harness, Jenkins), cloud MLOps platforms (Vertex AI, SageMaker), and data engineering tools (PySpark, Hive, Kafka).
Bachelor’s degree or equivalent experience.
Experienced at architecting scalable automated testing frameworks specifically for AI/ML and generative AI systems at enterprise scale.
Skilled in operationalizing model validation, adversarial testing, and embedding quality controls within CI/CD workflows in agile environments.
Capable of engaging cross-functional stakeholders effectively to align on AI quality risks, compliance, and performance benchmarks.