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Mid-level, popular Data Science/GenAI role in a metro location with broad skill requirements increases competition.
ML/GenAI and MLOps skills are transferable across industries, though domain-specific data experience moderately matters.
Explicit 3–6 years plus mandatory Python, PySpark, ML, and MLOps skills make shortlisting strict.
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Accountable for implementation, testing, validation, monitoring, and reporting of analytical solutions using large datasets and AI/ML tools.
Perform AI/ML model scoring, impact analysis, and continuous monitoring to ensure model accuracy and operational stability.
Develop automation and AI-driven tools to improve analytical processes and support MLOps/LLMOps practices, including deployment and governance of AI/ML solutions.
3 to 6 years of experience in Data Analytics or Data Science roles.
Strong programming skills in Python, PySpark, and SQL for data manipulation and production solution development.
Knowledge of LLM tools, GenAI concepts, and application of statistical and machine learning techniques.
Work Experience Required: 3 to 6 years; Notice period: Not explicitly mentioned in the JD.
Experienced in managing end-to-end AI/ML workflows including monitoring, observability, and automation frameworks.
Skilled in developing and maintaining AI/ML operational solutions with a focus on scalability and governance (MLOps/LLMOps).
Technically proficient in handling large-scale data environments using modern tools like Hadoop and workflow orchestration platforms, with added advantage for those with GenAI certifications.