AI Engineer
About the role
- Design, train, and optimize machine learning and deep learning models for various applications.
- Data Processing:
- Collect, clean, and preprocess large datasets for training and inference.
- Deployment:
- Implement and deploy AI models into production environments using scalable architectures.
- Research
- Innovation:
- Stay updated with the latest AI trends and integrate cutting-edge techniques into solutions.
- Collaboration:
- Work closely with data scientists, software engineers, and product teams to deliver AI-powered features.
- Performance Monitoring:
- Continuously monitor and improve model accuracy, efficiency, and reliability.
- Develop :Microsoft related AI Tools
- Proficiency in Python, Agentic AI(autogen), MCP, OpenAIStrong understanding of machine learning algorithms, NLP, and computer vision.
- Experience with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).Knowledge of data engineering tools and frameworks.
- Experience in code debugging, API Design knowledge
- Soft Skills:
- Problem-solving, analytical thinking, and excellent communication.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
