Job brief
We are looking for a visionary AI Engineer to join our core R&D team to build intelligent features that transform our user experience. In this role, you will be responsible for the full lifecycle of AI applications, from prototyping neural networks to monitoring production models for accuracy and performance. You will work alongside software engineers and product leaders to deploy machine learning solutions that process high-velocity data in real-time. If you are passionate about pushing the boundaries of what is possible with artificial intelligence, we invite you to help us shape our technical roadmap.
Key highlights
- Design and develop robust machine learning models, including LLMs, CNNs, and RNNs, to support personalized user experiences and predictive automation.
- Architect and maintain scalable MLOps pipelines using tools like Kubeflow or MLflow to automate model training, testing, and deployment cycles.
- Optimize deep learning models for inference latency and resource efficiency to ensure smooth performance across edge and cloud environments.
- Collaborate with data infrastructure teams to design data lakes and streaming architectures using Apache Kafka or Spark for real-time model ingestion.
What is a AI Engineer?
An AI Engineer is a specialized software architect focused on designing, training, and deploying machine learning models to solve complex business challenges. By leveraging frameworks like TensorFlow, PyTorch, and Keras, an AI Engineer transforms raw data into predictive insights and autonomous processes that power modern digital intelligence. Their expertise spans natural language processing (NLP), computer vision, and deep learning, making an AI Engineer essential for companies seeking to innovate through automation and algorithmic decision-making.
What does a AI Engineer do?
An AI Engineer spends their time engineering data pipelines, fine-tuning hyperparameters for deep learning models, and integrating inference engines into production-grade software. They conduct rigorous A/B testing on model performance, troubleshoot data drift issues, and collaborate with data scientists and DevOps teams to deploy scalable solutions using MLOps practices. Daily work involves writing efficient Python code, managing GPU clusters for model training, and building APIs that allow client applications to consume real-time predictions.
Key responsibilities
- Design and develop robust machine learning models, including LLMs, CNNs, and RNNs, to support personalized user experiences and predictive automation.
- Architect and maintain scalable MLOps pipelines using tools like Kubeflow or MLflow to automate model training, testing, and deployment cycles.
- Perform extensive feature engineering and data preprocessing on large-scale datasets stored in SQL, NoSQL, or cloud-based data warehouses like Snowflake.
- Optimize deep learning models for inference latency and resource efficiency to ensure smooth performance across edge and cloud environments.
- Analyze model accuracy and mitigate bias through rigorous statistical evaluation, experiment tracking, and comprehensive post-deployment monitoring.
- Collaborate with data infrastructure teams to design data lakes and streaming architectures using Apache Kafka or Spark for real-time model ingestion.
- Implement security protocols for AI systems to protect training data, fine-tuned weights, and proprietary model architectures from potential vulnerabilities.
- Document technical requirements, research findings, and model versioning specifications to ensure knowledge sharing across cross-functional engineering squads.
Requirements and skills
- Proven experience building and deploying production-ready machine learning models using Python, R, or C++ in a distributed computing environment.
- Deep expertise in modern deep learning frameworks such as PyTorch, TensorFlow, JAX, or Keras for research and development.
- Proficiency with cloud-based AI services, including AWS SageMaker, Google Vertex AI, or Azure Machine Learning for scalable model management.
- Strong understanding of NLP techniques, transformer architectures, and vector databases like Pinecone, Milvus, or Weaviate.
- Experience implementing containerization and orchestration workflows using Docker and Kubernetes for high-availability AI services.
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on statistics.
- Certification in AI or Machine Learning, such as AWS Certified Machine Learning – Specialty or Google Professional Machine Learning Engineer.
- Demonstrated ability to translate complex business problems into viable machine learning objectives for non-technical product stakeholders and executives.
FAQs
What does an AI Engineer do on a daily basis?
An AI Engineer works on the intersection of data science and software engineering by developing, testing, and deploying machine learning models. Daily tasks involve data cleansing, training neural networks, optimizing model performance using GPU resources, and building APIs that allow web applications to interact with trained models. They also dedicate time to monitoring production environments to ensure models remain accurate as real-world data patterns shift over time.
What skills are required to become an AI Engineer?
To become an AI Engineer, one must possess strong programming skills in Python or C++, alongside a deep understanding of linear algebra, calculus, and probability. Technical proficiency in frameworks like PyTorch or TensorFlow is mandatory, as is experience with MLOps tools like Docker, Kubernetes, and cloud platforms like AWS. Beyond technical skills, the role requires analytical problem-solving abilities and the capacity to explain complex model outcomes to business leaders.
Who does an AI Engineer work with on a technical team?
An AI Engineer typically acts as the bridge between data scientists, who define model logic, and DevOps engineers, who manage the infrastructure. They work closely with software engineers to integrate AI models into larger applications and coordinate with product managers to define the business value of new features. This highly collaborative environment requires regular interaction with data engineers to ensure the quality and accessibility of training data.
Why is the AI Engineer role critical for modern organizations?
The AI Engineer role is critical because they turn theoretical machine learning research into tangible, scalable software products that drive efficiency and revenue. By automating decision-making processes, identifying patterns in massive datasets, and improving user personalization, AI Engineers allow organizations to gain a competitive edge. Their ability to maintain secure, efficient, and accurate AI systems is fundamental to the digital transformation efforts of any tech-forward company.