Junior ML Engineer

0 - 2 years

1 - 5 Lacs

Posted:21 hours ago| Platform: GlassDoor logo

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Work Mode

Remote

Job Type

Part Time

Job Description

Junior Machine Learning Engineer Role
Key Responsibilities:
  • Machine Learning Development Support: Assist in designing, developing and deploying ML models and algorithms under the guidance of senior engineers, to tackle client challenges across banking, legal and related sectors.
  • Cloud & MLOps Support: Help implement ML solutions on AWS (with emphasis on Amazon SageMaker). Contribute to building and maintaining CI/CD pipelines using infrastructure-as-code tools such as CloudFormation and Terraform to automate model training and deployment.
  • Algorithm Implementation & Testing: Write clean, efficient Python code to implement ML algorithms and data pipelines. Conduct experiments, evaluate model performance (e.g. accuracy, precision, recall) and document results.
  • Collaboration & Communication: Work closely with data scientists, ML engineers and DevOps teams to integrate models into production. Participate in sprint meetings and client calls, conveying technical updates in clear, concise terms.
  • Quality, Documentation & Compliance: Maintain thorough documentation of data preprocessing steps, model parameters and deployment workflows. Follow data security best practices and ensure compliance with confidentiality requirements for financial and legal data.
Required Qualifications & Experience:
  • Education: Bachelor’s degree in Computer Science, Engineering, Data Science or a closely related discipline.
  • Experience: 0–2 years of practical exposure to machine learning or software development—this may include internships, academic projects or early professional roles.
  • Programming & ML Skills: Proficiency in Python (including pandas, NumPy, scikit-learn). Basic understanding of ML concepts and model evaluation techniques.
  • Cloud & DevOps Familiarity: Hands-on coursework or project experience with AWS (preferably SageMaker). Awareness of CI/CD principles and infrastructure-as-code tools (CloudFormation, Terraform).
  • Hybrid Work Skills: Comfortable operating in a hybrid environment—able to collaborate effectively onsite in Chennai and maintain productivity when working remotely.
  • Soft Skills: Strong analytical thinking, problem-solving aptitude and clear written/verbal communication. Demonstrated ability to learn quickly and work in a client-focused setting.

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