AI Engineer

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.

• 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.

• 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 Tensor Flow, 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, Dev Ops, 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).