Machine Learning Engineer / AI Engineer

We are seeking a Senior Machine Learning Engineer to lead the design, development, and production deployment of advanced AI and machine learning solutions. In this role, you will work across engineering, product, and data teams to solve complex business problems, advance modern ML practices, and ensure responsible and scalable AI systems. You will help foster a culture of technical excellence, experimentation, and continuous learning while working with state-of-the-art technologies. Responsibilities

• Lead the development, training, evaluation, and deployment of large-scale ML and AI models in production environments.
• Design and implement prompt engineering strategies, agentic workflows, and orchestration frameworks to support advanced AI use cases.
• Build and operate scalable ML systems using cloud platforms and distributed computing technologies.
• Establish and promote MLOps best practices, including model versioning, monitoring, CI/CD pipelines, and automated retraining.
• Partner closely with engineers, product managers, and data scientists to translate business requirements into robust ML solutions.
• Ensure responsible AI practices, including model governance, reliability, security, and ethical considerations.
• Communicate technical concepts and trade-offs clearly to both technical and non-technical stakeholders.
• Contribute to technical leadership through design reviews, mentoring, and continuous improvement of engineering standards.

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
• Proven experience designing, training, and deploying machine learning or AI models in production systems.
• Strong understanding of modern ML techniques and their application to real-world business problems.
• Experience working with at least one major cloud platform (AWS, Azure, or Google Cloud Platform).
• Excellent communication skills and the ability to collaborate in cross-functional environments., * Experience with prompt engineering, agentic systems, and AI orchestration frameworks.
• Hands-on experience with distributed systems and large-scale compute frameworks such as Kubernetes, Ray, or Slurm.
• Strong knowledge of MLOps tooling and practices, including MLflow, model monitoring, and CI/CD for ML workflows.
• Demonstrated technical leadership, including influencing architecture decisions and mentoring other engineers.
• Passion for detail, ownership, and delivering high-quality, reliable ML systems at scale.