Artificial Intelligence Engineer

Job Summary

We are looking for an AI Engineer with 2-3 years of hands-on experience to design, develop, and deploy AI models and systems. The ideal candidate will have expertise in large language models (LLMs), retrieval-augmented generation (RAG), optical character recognition (OCR), and multimodal AI technologies. You will collaborate with cross-functional teams to build scalable AI solutions that integrate text, image, and other data modalities to solve real-world problems, while ensuring adherence to security principles and working with fine-tuned or offline models.

Key Responsibilities

• Develop and optimize AI models using LLMs for natural language processing tasks, including fine-tuning and prompt engineering.
• Implement RAG pipelines to enhance model accuracy by integrating external knowledge sources and retrieval mechanisms.
• Design and integrate OCR systems for extracting and processing text from images and documents.
• Build multimodal AI applications that handle diverse data types such as text, images, audio, and video.
• Understand and apply security principles to AI systems, including data privacy, model robustness against adversarial attacks, and secure deployment practices.
• Fine-tune AI models for specific use cases and work with offline models to enable edge computing or privacy-preserving applications.
• Collaborate with data scientists, software engineers, and product teams to deploy AI solutions in production environments.
• Conduct experiments, evaluate model performance, and iterate on architectures to improve efficiency, scalability, and security.
• Stay updated with the latest advancements in AI technologies, security best practices, and offline model deployment, and apply them to ongoing projects.
• Troubleshoot and debug AI systems to ensure reliability, performance, and compliance with security standards.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 2-3 years of professional experience in AI engineering or a similar role.
• Proven experience working with LLMs (e.g., GPT, BERT, or similar frameworks like Hugging Face Transformers).
• Hands-on expertise in RAG techniques, including vector databases (e.g., Pinecone, FAISS) and knowledge retrieval systems.
• Strong proficiency in OCR technologies (e.g., Tesseract, EasyOCR, or cloud-based services like Google Vision API).
• Experience with multimodal AI models and frameworks (e.g., CLIP, DALL-E, or multimodal transformers).
• Solid understanding of security principles in AI systems, such as data encryption, secure model training, vulnerability assessment, and mitigation of risks like prompt injection or data leakage.
• Ability to fine-tune AI models using techniques like LoRA or PEFT, and experience working with offline models (e.g., deploying on local devices without internet dependency).
• Proficiency in programming languages such as Python, and familiarity with AI libraries like TensorFlow, PyTorch, or LangChain.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, neural networks, and data preprocessing.
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience with cloud platforms (e.g., AWS, Azure, GCP) for AI deployment, including secure environments.
• Knowledge of DevOps practices for AI/ML workflows (e.g., MLOps tools like Kubeflow or MLflow), with a focus on secure pipelines.
• Familiarity with ethical AI practices, bias mitigation in models, and compliance standards like GDPR or HIPAA.