Representative

Role DescriptionWe are seeking an innovative and technically proficient AI Engineer to design, develop, and deploy intelligent artificial intelligence solutions that solve complex business challenges and enhance operational efficiency. The successful candidate will be responsible for building machine learning and Generative AI applications, developing scalable AI systems, integrating AI models into production environments, and collaborating with cross-functional teams to deliver impactful AI-driven products. This role combines expertise in software engineering, machine learning, data science, and cloud technologies to create secure, reliable, and production-ready AI solutions.Key responsibilities include designing, developing, and deploying machine learning and Generative AI applications for business and customer-facing use cases; building, training, evaluating, and optimizing machine learning, deep learning, and natural language processing (NLP) models; developing AI-powered solutions such as intelligent assistants, chatbots, recommendation systems, predictive analytics platforms, computer vision applications, and automation tools; integrating foundation models and Large Language Models (LLMs) into enterprise applications through APIs and orchestration frameworks; implementing Retrieval-Augmented Generation (RAG), semantic search, vector databases, embeddings, and prompt engineering techniques to improve AI performance; developing scalable RESTful APIs, microservices, and backend services for AI applications; collaborating with software engineers, data scientists, product managers, and business stakeholders to translate business requirements into AI-driven solutions; preparing and processing datasets, feature engineering pipelines, and model evaluation frameworks; monitoring AI models in production to improve performance, reliability, scalability, and cost efficiency; implementing model versioning, testing, observability, and continuous deployment practices; ensuring AI systems comply with security, privacy, governance, and responsible AI principles; maintaining technical documentation, system architecture diagrams, and deployment procedures; researching emerging AI technologies and recommending innovative solutions; and contributing to continuous improvement of AI engineering standards, development workflows, and operational best practices.The AI Engineer is expected to combine strong software engineering capabilities with expertise in machine learning and artificial intelligence to deliver scalable, production-grade AI solutions. Success in this role requires analytical thinking, creativity, problem-solving abilities, effective communication, and a passion for applying cutting-edge AI technologies to real-world business challenges.QualificationsBachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Information Technology, or a related discipline.Strong understanding of machine learning, deep learning, artificial intelligence, natural language processing (NLP), computer vision, and Generative AI concepts.Proficiency in Python and experience with software development best practices.Experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, Keras, XGBoost, or similar technologies.Familiarity with Large Language Models (LLMs), transformer architectures, prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, embeddings, and vector databases.Experience using AI frameworks and orchestration tools such as Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, Semantic Kernel, DSPy, Haystack, or similar platforms.Knowledge of SQL, NoSQL databases, data engineering pipelines, and data processing techniques.Familiarity with cloud platforms including AWS, Microsoft Azure, Google Cloud Platform (GCP), or equivalent environments.Experience with Docker, Kubernetes, Git, CI/CD pipelines, and MLOps practices is advantageous.Understanding of RESTful APIs, microservices, distributed systems, and software architecture principles.Strong analytical, debugging, and problem-solving skills.Excellent written, verbal, and presentation communication skills with the ability to explain technical concepts to both technical and non-technical audiences.Knowledge of AI security, model evaluation, responsible AI, data privacy, governance, and compliance best practices.Professional certifications in Artificial Intelligence, Machine Learning, Cloud Computing, Data Science, or Software Engineering are considered advantageous but are not mandatory.Demonstrated commitment to continuous learning and staying current with advancements in machine learning, Generative AI, multimodal AI, MLOps, cloud-native AI technologies, and emerging industry best practices.