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Junior ML role in Bangalore with generalist requirements increases applicant density despite modest employer brand.
Modeling, MLOps, and LLM skills are broadly transferable across industries.
Multiple mandatory MLOps, LLM, cloud, and containerization requirements make screening fairly strict.
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Perform advanced statistical data analysis and build complex machine learning models using Python libraries like scikit-learn and TensorFlow.
Design and implement scalable machine learning pipelines and workflows, including MLOps practices using tools like MLflow and Kubeflow.
Communicate technical insights to both technical and non-technical stakeholders and mentor junior engineers within the team.
1+ years of experience in AI/ML/Gen AI engineering.
Advanced proficiency in Python and data science libraries (pandas, NumPy, scikit-learn, TensorFlow).
Experience with cloud platforms, preferably AWS.
Hands-on experience with MLOps tools (MLflow or Kubeflow) and containerization technologies such as Docker and Kubernetes.
Has demonstrated ability to architect complex machine learning solutions and implement end-to-end ML workflows in production environments.
Experienced working with Large Language Models (Gen AI) and Natural Language Processing.
Skilled in collaboration and communicating technical insights clearly to diverse stakeholders while mentoring junior engineers.