AI Engineer

Role Overview

We are seeking a highly skilled AI Engineer to architect, develop, and implement advanced artificial intelligence and machine learning systems. This role involves close collaboration with data scientists, software engineers, product teams, and business stakeholders to translate intricate business requirements into scalable AI solutions.

Key Responsibilities

• Design, develop, and deploy AI and machine learning models applicable to real-world business problems.
• Develop and fine-tune Generative AI, large language models (LLMs), natural language processing (NLP), and other ML-based solutions.
• Create and maintain production-grade AI pipelines and APIs.
• Leverage frameworks such as PyTorch, TensorFlow, Hugging Face, and LangChain for development work.
• Develop Retrieval-Augmented Generation (RAG), agentic AI, prompt engineering techniques, and conduct LLM evaluations.
• Integrate AI models into enterprise software systems, databases, and cloud environments.
• Optimize models for performance, scalability, stability, and cost-efficiency.
• Implement best practices for AI system development, testing, monitoring, and deployment.
• Provide mentorship to junior and mid-level AI engineers and participate in key technical decisions.
• Collaborate cross-functionally to identify impactful AI use cases and deliver business value.
• Stay up-to-date with latest AI research, technologies, and industry standards.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• Over 5 years’ professional experience in AI/ML engineering or a comparable technical discipline.
• Strong programming skills in Python and solid software engineering fundamentals.
• Proven experience building and deploying AI/ML models in production environments.
• In-depth understanding of machine learning and deep learning methodologies.
• Hands-on knowledge of LLMs, Generative AI, Retrieval-Augmented Generation, embeddings, vector databases, and AI agents.
• Experience working on cloud platforms like AWS, Azure, or Google Cloud.
• Proficiency with building APIs, microservices, Git version control, continuous integration and deployment (CI/CD), and containerization technologies including Docker and Kubernetes.
• Excellent analytical thinking, problem-solving skills, and communication abilities.

Preferred Qualifications

• Experience with MLOps/LLMOps and model monitoring tools.
• Background in enterprise AI system architecture and scalable solutions.
• Awareness of responsible AI practices, model governance, security, and data privacy considerations.
• Familiarity with Azure OpenAI, OpenAI APIs, or similar foundational AI model platforms.
• Contributions to publications, open-source AI projects, or demonstrable AI development work is a plus.