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Remote option, metro locations, known employer, and broad ML/platform skillset increase applicant competition.
ML engineering skills transfer across industries, but platform integration and MLOps needs moderate domain specificity.
Explicit 1+ year requirement and mandatory Python/ML skills moderate shortlisting rigidity.
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Integrate AI and machine learning capabilities into platform infrastructure to enhance security, efficiency, and EV charging experience.
Develop, test, and deploy machine learning models and APIs, ensuring transition from experimentation to production.
Collaborate with cross-functional teams to maintain data pipelines and automate AI-related processes.
1+ years experience in AI/ML engineering, platform engineering, or relevant academic/internship projects.
Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent practical experience.
Proficient in Python programming and ML libraries (NumPy, Pandas, scikit-learn).
Basic understanding of deep learning frameworks (PyTorch or TensorFlow) and exposure to NLP, LLMs, or computer vision projects.
Candidate who can effectively bridge AI development with platform engineering for scalable AI solutions.
Experience or interest in end-to-end ML projects, hackathons, or applied research.
Familiar with cloud platforms (AWS/Azure/GCP), version control (Git), containerization (Docker), or MLOps concepts.