Machine Learning Engineer

NOTE: To apply for this position, you should have a working project based on the skills mentioned below. You would need to submit screenshots of the running application. This is the only selection criteria.

Company Description AINE AI is a deep tech company that specializes in developing AI-enabled technology products. Our APIs and customizable hosted products allow legacy and developing software to integrate AI modules readily available in the market.

Key Responsibilities: * Model Development: Design, build, train, and fine-tune machine learning models to solve specific business problems. * Data Handling: Collaborate with data scientists and data engineers to manage and optimize data pipelines, ensuring data quality and availability for model training. * Deployment and MLOps: Deploy models into production, monitor their performance, ensure accuracy, and maintain the systems in an MLOps (Machine Learning Operations) environment. * Optimization & Scaling: Focus on optimizing model performance, ensuring scalability, reliability, and cost-effectiveness of AI solutions on the AWS cloud.

Required Skills and Qualifications: * Education: A Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field. * Experience: 2+ years of professional, non-internship experience in an AI Specialist, Data Scientist, or Machine Learning Engineer role. * Hands-on experience in

Python, SQL, and Excel . * Strong exploratory data analysis /

EDA skills — ability to identify patterns, anomalies, and insights from complex datasets. * Experience in end-to-end machine learning model development — data analysis, feature engineering, hyperparameter tuning, and validation — with working knowledge of algorithms such as

Random Forest, XGBoost, and SVM. * Hands-on experience working with

LLMs from providers such as OpenAI, Anthropic, and open-source alternatives, including prompt engineering techniques such as

zero-shot, few-shot, and chain-of-thought prompting. * Programming Languages: Strong proficiency in Python is essential, and experience with other languages like Java or R may be beneficial. * Familiarity with vector databases or

RAG (Retrieval-Augmented Generation) pipelines. * ML Frameworks: Hands-on experience with AI frameworks and libraries such as

TensorFlow, PyTorch, or Scikit-learn. * AWS Services:

Solid experience with core AWS services and specific AI/ML tools (e.g., S3, EC2, Lambda, and especially Amazon SageMaker) . * Problem-Solving: Strong analytical and problem-solving skills, with the ability to work with complex datasets.