Junior Platform Engineer

In this role, you will work closely with senior engineering and cross-functional teams to build and maintain data pipelines and machine learning integrations. Your day-to-day will involve writing clean Python code, developing APIs, and automating processes to transition AI solutions from experimentation into staging and production environments. Success in this position means effectively bridging the gap between AI development and platform engineering to deliver scalable, intelligent features.

What You Will Bring to ChargePoint

• Assist in developing, training, and fine-tuning machine learning, deep learning, and NLP models.
• Support senior engineers in designing, building, and maintaining robust data pipelines.
• Implement foundational preprocessing, feature engineering, and data validation processes.
• Contribute to testing, evaluating, and optimizing model performance for scalability and accuracy.
• Deploy machine learning models into staging and production environments under the guidance of senior team members.
• Write clean, modular, and well-documented Python code for AI experiments and platform prototypes.
• Develop APIs, model inference scripts, and automate small-scale tasks to streamline engineering workflows.
• Collaborate with cross-functional teams to translate business requirements into technical deliverables.
• Research and stay continuously updated with emerging AI tools, frameworks, and MLOps techniques.

Requirements

• 1+ years of 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.
• Solid programming skills in Python and foundational ML libraries (e.g., NumPy, Pandas, scikit-learn).
• Basic understanding of deep learning frameworks such as PyTorch or TensorFlow.
• Exposure to Natural Language Processing (NLP), Large Language Models (LLMs), or computer vision projects.
• Understanding of data structures, algorithms, and model evaluation metrics.
• Strong analytical problem-solving skills and a highly proactive willingness to learn new technologies.

Preferred Qualifications

• Hands-on experience with end-to-end ML projects, hackathons, or applied research work.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Knowledge of version control (Git), containerization (Docker), or basic MLOps concepts.
• Understanding of vector databases, embeddings, or Retrieval-Augmented Generation (RAG) architectures.