AI Engineer

We are looking for a hands-on AI Engineer with strong experience in building and implementing AI/ML solutions to solve real-world business problems. The ideal candidate should have practical experience developing AI/ML applications, with a strong preference for candidates who have deployed, maintained, or supported solutions in production environments. Candidates with experience in AWS, GCP, or Azure are welcome.

Responsibilities:

• Design, develop, and implement AI/ML solutions for real-world business use cases.
• Build and integrate AI/ML models into applications and business workflows.
• Develop and validate proof-of-concept (POC) solutions and help transition successful implementations into production.
• Deploy, maintain, and support AI/ML solutions in production environments.
• Collaborate with cross-functional teams to identify business requirements and deliver effective AI-driven solutions.
• Monitor and troubleshoot deployed solutions to ensure performance, reliability, and scalability.
• Continuously explore and apply relevant AI/ML techniques to improve solution quality and efficiency.

Requirements:

• 3-6 years of relevant experience in AI Engineering, Machine Learning, or AI/ML implementation.
• Strong hands-on experience in developing and implementing AI/ML solutions.
• Experience working on real-world AI/ML use cases.
• Exposure to at least one cloud platform: AWS, GCP, or Microsoft Azure.
• Understanding of the AI/ML solution development lifecycle, including development, testing, deployment, and maintenance.
• Ability to translate business requirements into practical AI/ML solutions.

Preferred Qualifications:

• Experience building, deploying, or supporting AI/ML solutions in production environments.
• Experience with end-to-end AI/ML implementation, from development and validation to deployment and monitoring.
• Experience transitioning POCs into production-ready solutions.
• Practical experience solving business problems using AI/ML technologies.

Skills

AI, AWS, Azure, GCP, ML, deployment, machine learning, production