We are seeking a mid-level AI Engineer to join our team focused on building scalable, production-ready AI solutions that support analytics, automation, and intelligent decision-making.

This role will contribute to the development and deployment of machine learning models, generative AI systems, and AI-enhanced data workflows across platforms.

Key Responsibilities :

- Model Development & Deployment : Design, train, and deploy machine learning and deep learning models using frameworks like TensorFlow, PyTorch, and Scikit-learn.

- Generative AI & LLMs : Implement and fine-tune large language models (LLMs) using tools such as LangChain, RAG, and vector databases.

Experience with GPTs, LLaMA, or similar models is preferred

- MLOps & GenAIOps : Use tools like MLflow and Docker to manage model lifecycle, reproducibility, and scalability.

Support production-grade GenAI systems

- Data Engineering Collaboration : Work closely with data engineers to ensure robust data pipelines and infrastructure for model training and inference.

- Integration & APIs : Develop APIs and microservices to integrate AI models into enterprise applications and workflows.

- Monitoring & Optimization : Continuously monitor model performance and retrain as needed to maintain accuracy and relevance.

- Security & Governance : Ensure AI systems comply with enterprise security and data governance standards.

Skills & Qualifications :

- 35 years of experience in AI/ML engineering or related roles.

- Proficiency in Python and experience with AI frameworks (TensorFlow, PyTorch).

- Familiarity with cloud platforms (AWS, Azure, GCP) for model deployment.

- Experience with MLOps tools (MLflow, Docker) and GenAI deployment.

- Strong understanding of LLMs, NLP, and computer vision techniques.

- Ability to write clean, efficient, and reusable code.

- Experience with RESTful APIs and microservices architecture.

Preferred Experience :

- Exposure to educational technology or enterprise data environments.

- Experience integrating AI into transactional systems.

- Familiarity with data warehouses and data governance frameworks.