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Tier-1 employer, mid-level seniority, metro context, and broad technical requirements drive high competition.
Requires specialized AI/ML validation, MLOps, and data-pipeline expertise, limiting cross-industry transferability.
Explicit 5–8 years, mandatory AI validation experience, broad technical stack, and regulated banking compliance increase strictness.
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Design, implement, and maintain scalable automated testing frameworks for AI/ML systems including LLMs and RAG pipelines.
Lead validation of AI models, data pipelines (ETL), and software integrations focusing on metrics like hallucination rates and semantic similarity.
Integrate AI quality gates into CI/CD pipelines and lead adversarial testing to ensure compliance with ethical AI guidelines and regulatory standards.
5-8 years of professional experience in data-intensive solutions or test automation frameworks, with 2-3 years in AI/ML or Generative AI validation.
Proficient in Python, Shell scripting, and familiar with Java and AI/ML evaluation frameworks (e.g., Scikit-learn, TensorFlow, RAGAs, Hugging Face Transformers).
Experience with MLOps and lifecycle platforms such as MLFlow, Dagster, Vertex AI, and SageMaker.
Bachelor's degree or equivalent experience is mandatory.
Experienced in cross-functional collaboration between data science, software engineering, and quality assurance teams at enterprise scale.
Skilled in operationalizing complex AI models and building automated validation services integrated into CI/CD environments.
Strong understanding of adversarial testing techniques and operational monitoring for AI systems to maintain model performance and ethical compliance.