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We are seeking an AI Engineer to collaborate with cross-functional teams in solving compliance and regulatory business challenges through innovative AI-powered solutions. You will contribute across the full lifecycle of AI model development—from problem definition to deployment—ensuring high-performance, scalable, and ethically responsible AI solutions.

In this role, you will work on model development, training, fine-tuning, prompt engineering, and continuous improvement based on real-world performance and stakeholder feedback. You will design, build, test, and integrate AI-powered applications while adhering to best software engineering practices. Effective communication with both technical and non-technical stakeholders is essential to align project goals and deliverables.

Responsibilities

Collaborate with cross-functional teams to address compliance and regulatory business problems using AI-driven solutions.
Contribute to the entire AI model lifecycle, including problem definition, development, training, fine-tuning, deployment, and ongoing improvements.
Develop, test, and integrate AI-powered applications with strong adherence to software engineering best practices.
Implement and refine RAG-based generative AI solutions and prompts.
Continuously evaluate and improve model performance using real-world feedback.
Communicate effectively with stakeholders across technical and business domains.

Requirements

3–5 years of experience in software engineering or a related role, with exposure to AI/ML concepts and applications.
Strong proficiency in Python for production-level software development.Hands-on experience in application development, including building and deploying APIs.
Familiarity with AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, and OpenAI API, and their integration into applications.
Experience with RAG-based generative AI solutions and strong knowledge of prompt engineering.
Understanding of machine learning concepts, including model types, training, fine-tuning, and deployment.
Knowledge of MLOps best practices (model lifecycle management, monitoring, scalability) is a plus.
Experience with source control, DevOps, and CI/CD pipelines (Git, Docker, Kubernetes).
Strong understanding of software engineering principles, including scalability, performance optimization, and maintainability.
A collaborative team player with a proactive approach to problem-solving and adaptability to new technologies.
Degree in Computer Science, Software Engineering, Data Science, or a related field (or equivalent experience).

Competencies

Digital: Python
Digital: Machine Learning
Digital: Artificial Intelligence (AI)

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