AI Engineer

We are seeking a highly skilled and innovative AI Engineer to join our forward-thinking Technology team. In this role, you will bridge the gap between software engineering and data science, taking responsibility for designing, developing, and deploying cutting-edge Artificial intelligence and machine learning models. You will work cross-functionally to integrate intelligent, data-driven features into our core products and optimize them for Scale and performance. PRIMARY DUTIES AND

Responsibilities

• Model Development & Training: Design, build, and optimize machine learning and deep learning models (e.g., Natural Language Processing, Computer Vision, Predictive Analytics) tailored to specific business needs
• Data Engineering & Pipelines: Construct and maintain robust data pipelines to gather, clean, and preprocess large, complex datasets for efficient model training and inference
• Deployment & Integration: Deploy AI models into production environments, ensuring they are highly available and scalable
• Integrate models seamlessly with existing software architectures via robust RESTful APIs or gRPC. MLOps & Monitoring: Implement monitoring solutions to track model performance in real-time, troubleshoot issues, and automate retraining pipelines to combat data drift and degradation
• Research & Innovation: Stay continuously abreast of the latest advancements in AI methodologies, generative models (such as LLMs), and framework updates to elevate our technology stack
• Collaboration: Work closely with product managers, data scientists, and backend engineers to translate complex business requirements into tangible technical AI solutions

Desired Candidate Profile

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Mathematics, Statistics, or a closely related quantitative field. Proficiency in Python and strong familiarity with object-oriented languages such as C++, Java, or Go. Deep hands-on experience with modern frameworks and libraries, including TensorFlow, PyTorch, Keras, and Scikit-Learn. Solid understanding of software development life cycles (SDLC), version control (Git), CI/CD pipelines, and writing clean, testable code. Experience working with SQL, NoSQL databases, and data manipulation tools (Pandas, NumPy, Apache Spark). Experience working with Large Language Models (LLMs), prompt engineering, and fine-tuning techniques (e.g., LoRA, QLoRA, RAG architectures). Familiarity with cloud platforms and their ML ecosystems (AWS SageMaker, Google Cloud Vertex AI, or Azure Machine Learning). Strong background in containerization and orchestration tools (Docker, Kubernetes). A proven track record of bringing ML models from research/prototype phase to a live production environment.