AI Engineer
Remote · full time
Practice the AI Engineer interview with an AI interviewer: voice, live coding and a scorecard.
Skills the interview covers
- Machine Learning & Deep Learning
- Natural Language Processing (NLP)
- Computer Vision
- Neural Networks (CNN, RNN, Transformers, GANs)
- Data Engineering & Feature Engineering
- Python, TensorFlow, PyTorch, Scikit-learn, Hugging Face
- Cloud Platforms (AWS, GCP, Azure)
- Big Data Tools (Spark, Hadoop, Kafka)
- MLOps & Deployment (Docker, Kubernetes, CI/CD)
- Problem Solving & Analytical Thinking
About the role
We are seeking a highly skilled, innovative, and motivated AI Engineer to join our fast-growing technology team. In this role, you will play a pivotal part in shaping the future of our AI-driven solutions. The AI Engineer will be responsible for researching, designing, building, training, deploying, and maintaining artificial intelligence and machine learning systems that solve complex real-world problems and create value for our business.
You will work at the intersection of data science, machine learning, and software engineering, collaborating with diverse teams including product managers, business stakeholders, data scientists, cloud engineers, and backend/frontend developers. The solutions you create will directly impact product innovation, operational efficiency, decision-making, and user experience across multiple industries.
This role requires a strong background in machine learning algorithms, deep learning architectures, and statistical modeling techniques, along with practical experience in managing the end-to-end lifecycle of AI/ML projects — from data preprocessing and feature engineering to model deployment and monitoring in production environments.
The ideal candidate should be not only technically strong but also curious, adaptable, and passionate about staying on top of cutting-edge advancements in artificial intelligence, natural language processing, computer vision, and reinforcement learning. If you are excited by the challenge of pushing the limits of intelligent systems and building scalable AI-powered products, this role is for you.
Key Responsibilities
Research & Development: Explore, design, and implement novel AI/ML algorithms and solutions that address real-world business challenges.
Data Handling: Work with large-scale structured and unstructured datasets, including text, images, video, and sensor data, ensuring data quality, preprocessing, and transformation.
Model Development: Build, train, validate, and fine-tune machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn, or Hugging Face.
Deployment & MLOps: Implement scalable deployment pipelines using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure) while integrating with CI/CD workflows.
Applied AI: Develop solutions in areas such as natural language processing (chatbots, sentiment analysis, LLM-based systems), computer vision (image recognition, object detection), and recommendation systems.
Optimization & Monitoring: Continuously monitor, test, and refine deployed models to improve accuracy, robustness, and efficiency while addressing issues such as bias and fairness.
Collaboration: Work closely with product managers and cross-functional teams to integrate AI systems into customer-facing products and backend platforms.
Documentation & Best Practices: Produce clear documentation, follow best coding practices, and maintain reproducibility and scalability in AI pipelines.
Innovation: Stay ahead of emerging technologies, tools, and frameworks in AI/ML and share insights with the team to drive innovation.
Minimum Qualifications
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.
3+ years of professional experience in AI/ML engineering, with proven hands-on experience in developing and deploying machine learning models.
Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Familiarity with data pipelines, big data frameworks (Spark, Hadoop), and relational/noSQL databases.
Experience with cloud computing platforms such as AWS, Google Cloud Platform, or Microsoft Azure.
Strong understanding of neural networks, supervised and unsupervised learning, and statistical modeling.
Preferred Qualifications
Master’s or PhD in AI, Machine Learning, or a related field.
Experience with Natural Language Processing (transformer models, large language models).
Hands-on experience in Computer Vision projects.
Knowledge of Reinforcement Learning and Generative AI techniques (GANs, Diffusion Models).
Experience with MLOps tools like MLflow, Kubeflow, or SageMaker.
Publications in AI/ML conferences or open-source contributions.