AI Engineer

At Rival, we’re transforming how companies hire, onboard, and grow talent by making HR processes smarter, faster, and more agile. Our suite is purpose-built for HR teams and simplifies talent acquisition, modernizes HR workflows, and accelerates workforce productivity.

Our team is seeking an AI Engineer who designs, develops, and implements artificial intelligence (AI) systems and applications that can simulate human intelligence processes through the creation and validation of algorithms, neural networks, and other machine learning (ML) techniques. This role develops and trains artificial intelligence tools to help automate processes for Rival’s software solutions and ensures data integrity and system functionality. 

Your contributions to Rival will include:

• Designs and Develops AI Models: Building and optimizing ML and deep learning models for various applications such as natural language processing (NLP), computer vision, and predictive analytics.
• Copilot Development and Enhancement:  Developing and fine-tuning chatbots, enabling accurate and contextually aware responses to support business needs, and leveraging Gen AI to discern insights from data and render graphs and charts for user queries in chatbots.
• Data Analysis and Preprocessing: Working with large datasets to clean, preprocess, and prepare data for model training, ensuring high data quality and readiness for machine learning pipelines.
• Model Deployment and Scalability: Deploying AI models to production environments, ensuring they are scalable, efficient, and integrated with the existing systems and platforms (e.g., cloud infrastructure).
• Algorithm Optimization: Improving model performance through hyperparameter tuning, cross-validation, and model optimization techniques to ensure high accuracy, efficiency, and robustness.
• Collaboration with Cross-functional Teams: Partnering with data scientists, software engineers, and business stakeholders to understand product needs and translate them into AI-driven solutions.
• AI Research and Innovation: Staying updated on the latest trends and advancements in AI, implementing cutting-edge techniques to improve AI systems and processes.
• Monitor Model Performance: Continuously testing machine learning tools; tracking the performance of deployed models, ensuring their accuracy over time, and retraining models as needed to resolve issues and maintain performance.
• Automation and Efficiency: Automating machine learning workflows using tools and best practices, including the deployment of CI/CD pipelines for AI models.

Required Skills

• Ability to develop and deploy machine learning and AI models into production, ensuring real-time performance and scalability.
• Exceptional analytical, critical thinking and communication skills.
• Excellent organizational skills with ability to manage multiple projects simultaneously.
• Ability to adjust and thrive in a dynamic environment with changing priorities.
• Able to apply current AI governance practices, adhering to all practices and ensuring fairness in AI algorithms.

Required Experience

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science or related field is required; Masters degree preferred.
• 2-3 years of experience working as an AI Engineer, Machine Learning Engineer or related role, with in-depth knowledge of AI, generative AI models, AI governance practices, machine learning, natural language processing and associated technologies.
• Copilot Development: Experience fine-tuning generative AI models (such as GPT, BERT, etc.) and proficiency implementing Retrieval-Augmented Generation (RAG) architectures that combine large language models (LLMs) with real-time information retrieval systems is required.
• Programming Languages: Expertise in Python and SQL is required; familiarity with R, Java & JavaScript is a plus.
• Machine Learning Frameworks: Proficiency in TensorFlow, PyTorch, Keras, and Scikit-learn for training and deploying models is required.
• Version Control: Familiarity with Git for tracking code changes and managing model versions.
• DevOps for AI: Familiarity with containerization (Docker, Kubernetes) and building CI/CD pipelines for AI model deployment.
• AI Agents: Familiarity with deploying and managing AI agents capable of autonomously completing complex tasks through iterative learning and user interaction is preferred.
• Data Processing Tools:  Experience with data processing tools such as Pandas and NumPy is preferred.
• Cloud-Based Environment: Experience with cloud-based AI development and deployment is required, preferably with AWS is preferred.